Add collage-i2v experiment: wan2.6 3-case run + Physics-IQ scorer/results + base-model survey + per-axis judgment

Collage (composite demo storyboard onto query init frame) i2v experiment on physiq_prelim.
- wan2.6-i2v-flash on 3 cases (0065 compression / 0003 collision / 0035 ramp), 720P/no-audio
- Reference-based Physics-IQ scorer + budget-matched ST-IoU results (wan >= cosmos3, 3/3)
- Base-model survey (VAP/UniVideo/VINO/LoomVideo/VACE: base model + repo + finetune fit)
- Qualitative per-axis judgment (zero appearance-contamination, static top panel)
GT 4K videos not duplicated (already in data/physiq_prelim/gt_data).

collage_experiment/README.md

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+# Physical-ICL — Collage (拼图) i2v experiment + base-model survey
+
+In-context physical video generation on the **Physics-IQ preliminary subset** (`physiq_prelim`).
+Two deliverables:
+
+1. **Collage-i2v run**: composite the demonstration storyboard onto the query init frame
+   (a single "拼图/collage" image) and feed it to an image-to-video model. This bypasses the
+   reference-video (r2v) *appearance-copy* trap by giving the model one composite image as the
+   sole visual prompt. We run **wan2.6-i2v-flash** on 3 key cases and evaluate vs the real GT
+   continuation, alongside the **cosmos3-Nano** collage baseline.
+2. **Base-model survey**: which open-source video-gen model is the strongest + easiest to
+   finetune for demo→query ICL TIV2V. See `research/basemodel_survey.md`.
+   **Shortlist: LoomVideo (lightest, has training code) / VAP (cheapest post-train) / UniVideo
+   (benchmark-backed).**
+
+---
+
+## Cases (good_follow config)
+
+| task | physics invariant |
+|---|---|
+| task_0065 | compression / deformation (dumbbell + block dropped onto pillows) |
+| task_0003 | collision / momentum transfer (ball rolls into rubber duck) |
+| task_0035 | gravity + rolling down a ramp |
+
+Inputs come from `yqi19/in-context-learning-cosmos3-output` (good_follow): each case's
+`input_composite.png` is the init image, `prompt_fed.txt` is the text prompt. wan output is
+720P (900×1022, the model's minimum), 5s, no audio.
+
+---
+
+## Quantitative: reference-based Physics-IQ vs GT
+
+Scorer: `scorer/physics_iq_score.py`. Results: `scorer/physics_iq_results.json`.
+
+The generated video's **bottom query panel** (y ≥ 0.495·H, ratio-cropped so it adapts to any
+resolution) is compared frame-by-frame against the real Physics-IQ GT continuation over the
+matched 5-second window (both 30 fps).
+
+### ⚠️ Metric caveat (important — read before trusting numbers)
+
+The **naïve raw-pixel IoU initially gave the WRONG answer** (cosmos3 > wan). Root cause: we are
+comparing *generated pixels* against *real 4K GT* — generation flicker + lighting/texture
+domain-gap produce diffuse per-pixel change that crosses the motion threshold in the *wrong*
+locations, inflating the generated video's active area (e.g. task_0065 wan active-pixel fraction
+0.201 vs GT 0.078). Naïve IoU then measures *motion quantity / flicker*, not physics.
+
+**Fix** (closer to the canonical Physics-IQ protocol): equalize each frame's motion budget to
+GT's active-pixel count (top-k selection), Gaussian-denoise, and morphological-open before
+computing IoU. The resulting **budget-matched spatiotemporal IoU** measures *localization at
+equal motion budget* and reverses the naïve result — now agreeing with the qualitative per-axis
+read on all 3 cases.
+
+### Budget-matched spatiotemporal IoU (higher = better)
+
+| task | wan collage-i2v | cosmos3 collage |
+|---|---|---|
+| task_0065 compression | **0.348** | 0.337 |
+| task_0003 collision   | **0.286** | 0.050 |
+| task_0035 ramp roll   | **0.063** | 0.041 |
+
+**wan ≥ cosmos3 on 3/3.** (FOV alignment verified — `scorer/fov_sanity_all.png` shows the wan
+query panel and GT share the same scene framing on all 3, so this is not a reframing artifact.)
+
+> Note: these are *raw* Physics-IQ components, not the population-normalized 0–100 Physics-IQ
+> Score (which needs a second real take per task for real-vs-real variance normalization). On
+> n=3 with a generated-vs-real domain gap, always pair budget-matched ST-IoU with the
+> qualitative per-axis judgment below.
+
+---
+
+## Qualitative per-axis judgment (Vita)
+
+**Setup.** Each query is generated by feeding the *collage* image (top = demo 3×3
+event storyboard, bottom = query first frame) as the **single `img_url`** to
+`wan2.6-i2v-flash` (720P, no audio) with an ICL prompt ("apply the demonstrated
+physics to the bottom query panel, keep the top panel static, do not copy the
+demo's appearance"). No `reference_video` channel is used — by construction this
+sidesteps the r2v appearance-copy failure mode. Judgment is read directly off the
+cropped bottom **query panel** (ratio y ≥ 0.495·H, resolution-agnostic) at full
+resolution, per physics axis, against GT and the Cosmos3-Nano collage output.
+
+### Per-case verdicts
+
+**task_0065 — compression / deformation — 🟢 STRONG POSITIVE**
+- Falling dynamics: ✅ both the kettlebell (30lb) and the green block are released and fall.
+- Compression-deform (target axis): ✅ the kettlebell lands on the left pillow and the pillow **visibly compresses/indents** under the load; the block lands flat on the right pillow. Best match to GT.
+- vs Cosmos3: Cosmos3 only dropped the kettlebell (block stayed suspended) → **wan > Cosmos3**.
+- Appearance-contamination: **none** ✅. Top demo panel stayed **static** ✅.
+
+**task_0035 — gravity / ramp rolling — 🟢 STRONG POSITIVE**
+- Gravity + rolling + redirection: ✅ the marble descends the cascade of colored ramps in sequence, redirecting at each ramp — the rule transferred cleanly.
+- Appearance-contamination: **none** ✅.
+
+**task_0003 — collision / momentum — 🟡 PARTIAL**
+- Rolling + contact: ✅ the ball rolls across the table and reaches (contacts) the duck.
+- Momentum transfer (target axis): ⚠️ **weak** — the duck is barely displaced; the collision resolves as "arrival/contact" rather than a clear knock-away.
+- Appearance-contamination: **none** ✅.
+
+### Cross-cutting findings
+1. **Zero appearance-contamination (3/3).** Collage-as-single-`img_url` i2v fully avoids the r2v appearance-copy disease that made the demo *harmful* in the earlier pilot. This is the headline win.
+2. **Top demo panel stays static** in wan, and there is no tail-frame collapse — unlike Cosmos3-Nano, which animated the whole composite and degraded the final frames.
+3. **Transfer strength is invariant-dependent:** deformation (0065) and gravity/rolling (0035) transfer **strongly**; collision-momentum (0003) transfers only to the contact stage (**weak** knock-away).
+4. **wan-collage ≥ Cosmos3-collage** on identical inputs (clearest on 0065), consistent with the budget-matched ST-IoU numbers.
+
+### Not tested / next
+- **Signed steering** (does an *opposite* demo push the physics the wrong way?) was not run. task_0003 has an `opposite_typed` config available — a reverse-demo run + signed per-axis read would test conditioning directionality.
+- n=3, single seed. Scaling to the core good_follow/good_rule set (86 cases) would firm up the per-invariant pattern.
+
+Assets: `judge/` (per-case GT / cosmos3 / wan query strips, `task_0065_3way.png` comparison, `task_0003_wan_query_zoom.png` full-res zoom).
+
+---
+
+## Repository layout
+
+```
+research/basemodel_survey.md   # VAP / UniVideo / VINO / LoomVideo / VACE (base model + GitHub)
+scorer/physics_iq_score.py     # reference-based Physics-IQ component scorer
+scorer/physics_iq_results.json # 3-case results + FOV check
+scorer/fov_sanity_all.png      # wan-panel vs GT FOV alignment check
+gen/build_collage.py           # collage (storyboard→query) builder
+gen/run_collage_v2.py          # wan2.6 collage-i2v runner
+gen/crop_query_panel.py        # ratio-based query-panel crop (shared by judging + Physics-IQ)
+judge/                         # GT / cosmos3 / wan strips + comparison figures
+videos/                        # 3 wan2.6-i2v-flash raw outputs (900×1022, 5s, no audio)
+```
+
+## Takeaway
+
+wan2.6 **collage-i2v works**: it transfers compression and gravity-rolling physics strongly,
+collision momentum weakly (contact but no knock-away), with **zero appearance contamination**
+(3/3) and a static top demo panel. Both the budget-matched quantitative metric and the
+qualitative per-axis read agree it beats the cosmos3-Nano collage baseline.
+
+For a **finetuned ICL base**, the survey (`research/basemodel_survey.md`) shortlists, by fit:
+**LoomVideo** (lightest at 5B, ships training code, interleaved multi-image+text — try first),
+**VAP** (cheapest to start post-training: freeze base + train only the MoT expert, ships
+VAP-Data; native demo→query in-context), and **UniVideo** (benchmark-backed but heavier). VINO
+is the best architectural fit but has no training code yet.

collage_experiment/gen/build_collage.py

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+#!/usr/bin/env python3
+"""Build 'puzzle/collage' visual-ICL init images for Physical-ICL.
+
+Siyuan's ask: paste the demo *example* frames onto the query init_frame in a
+single image, so wan2.6-i2v-flash gets the demonstration spatially (as one
+picture) instead of via the r2v reference-video channel (which just copies the
+demo's appearance).
+
+Two layouts produced per case:
+  A) stacked   : [demo 3x3 storyboard]  on TOP of  [query init]        (放上面, most literal)
+  B) filmstrip : [demo start | demo end | QUERY init]  side by side    (compact, keeps query big)
+
+Usage: python3 build_collage.py task_0001 [task_0035 ...] --demo good
+"""
+import argparse, os
+from pathlib import Path
+from PIL import Image, ImageDraw, ImageFont
+
+DATA_ROOT = "/tmp/physical_icl/repo/data/physiq_prelim/gt_data"
+OUT = Path("/tmp/physical_icl/collage")
+OUT.mkdir(parents=True, exist_ok=True)
+
+def font(sz):
+    for f in ["/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf"]:
+        if os.path.exists(f):
+            return ImageFont.truetype(f, sz)
+    return ImageFont.load_default()
+
+def label_bar(w, text, h=46, bg=(20,20,20), fg=(255,255,255)):
+    bar = Image.new("RGB", (w, h), bg)
+    d = ImageDraw.Draw(bar)
+    d.text((14, h//2), text, fill=fg, font=font(26), anchor="lm")
+    return bar
+
+def storyboard_frames(demo_png):
+    """Slice the 3x3 numbered storyboard back into 9 frames (drop number tag corner)."""
+    im = Image.open(demo_png).convert("RGB")
+    W, H = im.size
+    cw, ch = W//3, H//3
+    frames = []
+    for r in range(3):
+        for c in range(3):
+            frames.append(im.crop((c*cw, r*ch, (c+1)*cw, (r+1)*ch)))
+    return frames  # 9 frames, temporal order
+
+def build(task, demo_type):
+    root = Path(f"{DATA_ROOT}/{task}/episode_0001")
+    init = Image.open(root/"prompt"/"init_frame.png").convert("RGB")
+    demo_png = root/"demos"/f"{demo_type}_demo_01.png"
+    board = Image.open(demo_png).convert("RGB")
+    frames = storyboard_frames(demo_png)
+
+    TARGET_W = 1280
+    # normalize widths
+    def rw(im, w):
+        return im.resize((w, round(im.height*w/im.width)))
+
+    # ---- Layout A: stacked (demo storyboard on top of query) ----
+    b = rw(board, TARGET_W)
+    q = rw(init, TARGET_W)
+    lb1 = label_bar(TARGET_W, "EXAMPLE  (reference physics: watch the interaction)")
+    lb2 = label_bar(TARGET_W, "QUERY  (animate THIS scene with the SAME physics)")
+    Ha = lb1.height + b.height + lb2.height + q.height
+    A = Image.new("RGB", (TARGET_W, Ha), (255,255,255))
+    y=0
+    for part in (lb1, b, lb2, q):
+        A.paste(part, (0,y)); y+=part.height
+    pa = OUT/f"{task}_{demo_type}_A_stacked.png"
+    A.save(pa)
+
+    # ---- Layout B: filmstrip (demo start | demo end | query) 16:9-ish ----
+    cell_w = 640
+    fs = rw(frames[0], cell_w)   # demo before
+    fe = rw(frames[-1], cell_w)  # demo after
+    qb = rw(init, cell_w)        # query
+    ch = max(fs.height, fe.height, qb.height)
+    lbh = 40
+    B = Image.new("RGB", (cell_w*3, ch+lbh), (255,255,255))
+    for i,(im,txt) in enumerate([(fs,"EX: before"),(fe,"EX: after"),(qb,"QUERY -> animate")]):
+        B.paste(label_bar(cell_w, txt, h=lbh), (i*cell_w,0))
+        B.paste(im, (i*cell_w, lbh))
+    pb = OUT/f"{task}_{demo_type}_B_filmstrip.png"
+    B.save(pb)
+
+    print(f"{task}: A={pa.name} {A.size}  B={pb.name} {B.size}")
+    return pa, pb
+
+if __name__ == "__main__":
+    ap = argparse.ArgumentParser()
+    ap.add_argument("tasks", nargs="+")
+    ap.add_argument("--demo", default="good")
+    a = ap.parse_args()
+    for t in a.tasks:
+        build(t, a.demo)

collage_experiment/gen/crop_query_panel.py

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+#!/usr/bin/env python3
+"""Crop the bottom QUERY panel from an 832x944 collage-i2v output and make a
+temporal frame strip for per-axis judging. Query panel = y>=467 (bottom 477px).
+
+Usage: python3 crop_query_panel.py <video.mp4> <out_prefix> [n_frames=6]
+Outputs: <out_prefix>_query_strip.png (+ per-frame pngs in <out_prefix>_frames/)
+"""
+import sys, os, subprocess, json
+from pathlib import Path
+from PIL import Image
+
+QY0 = 467  # storyboard/query split for 832x944 composites (storyboard top ~467px)
+
+def probe(v):
+    r = subprocess.run(["ffprobe","-v","quiet","-print_format","json","-show_streams",v],
+                       capture_output=True, text=True)
+    s = [x for x in json.loads(r.stdout)["streams"] if x["codec_type"]=="video"][0]
+    return int(s["width"]), int(s["height"]), int(s.get("nb_frames") or 0)
+
+def main():
+    v = sys.argv[1]; pref = sys.argv[2]; n = int(sys.argv[3]) if len(sys.argv)>3 else 6
+    W,H,NF = probe(v)
+    qy0 = QY0 if H==944 else round(H*467/944)  # scale split if H differs
+    fr = Path(pref+"_frames"); fr.mkdir(parents=True, exist_ok=True)
+    NF = NF or 121
+    idxs = [round(i*(NF-1)/(n-1)) for i in range(n)]
+    crops = []
+    for i in idxs:
+        raw = fr/f"raw_{i}.png"
+        subprocess.run(["ffmpeg","-y","-loglevel","quiet","-i",v,"-vf",f"select=eq(n\\,{i})",
+                        "-vframes","1",str(raw)], check=False)
+        if raw.exists():
+            im = Image.open(raw).convert("RGB")
+            q = im.crop((0, qy0, im.width, im.height))  # bottom query panel
+            qp = fr/f"query_{i}.png"; q.save(qp); crops.append(q)
+    if crops:
+        w = crops[0].width; h = crops[0].height
+        strip = Image.new("RGB",(w*len(crops), h),(255,255,255))
+        for i,c in enumerate(crops): strip.paste(c,(i*w,0))
+        out = pref+"_query_strip.png"; strip.save(out)
+        print(f"query panel crop y>={qy0} (video {W}x{H}, {NF}f) -> {out} {strip.size}, frames {idxs}")
+    else:
+        print("no frames extracted")
+
+if __name__ == "__main__":
+    main()

collage_experiment/gen/run_collage_v2.py

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+#!/usr/bin/env python3
+"""Fire wan2.6-i2v-flash on a collage/puzzle visual-ICL image, using the repo's
+verified Wan26Generator (720P / no audio / single shot -> cost-safe)."""
+import os, sys, json, time, importlib.util
+from pathlib import Path
+
+REPO = "/media/yu/Extreme SSD/siyuan_project/repos/MLLM-as-World-Judge"
+sys.path.insert(0, REPO)
+os.chdir(REPO)
+from inference.wan_generator import Wan26Generator
+spec = importlib.util.spec_from_file_location("rap", os.path.join(REPO, "run_api_parallel.py"))
+rap = importlib.util.module_from_spec(spec); spec.loader.exec_module(rap)
+
+KEYS = json.load(open("/tmp/dashscope_keys.json"))
+OUT = Path("/tmp/physical_icl/gen/collage"); OUT.mkdir(parents=True, exist_ok=True)
+
+PROMPT_STACKED = (
+    "This image is a visual in-context example composed of two parts. "
+    "The TOP part (labeled EXAMPLE) is a reference demonstration showing a physical "
+    "interaction: an object is released, falls under gravity, and deforms the soft "
+    "surface it lands on. The BOTTOM part (labeled QUERY) is the scene to animate. "
+    "Generate a physically plausible continuation of ONLY the bottom QUERY scene: the "
+    "two grabber tools let go of the tennis ball and the orange block, which fall onto "
+    "the two pillows and press/deform them, following the same kind of soft-surface, "
+    "gravity-driven physics shown in the example. Keep the top EXAMPLE panels unchanged. "
+    "Do not copy the example's objects, colors, or background. Static camera, no camera movement."
+)
+
+def run(task, image, layout="A"):
+    # DashScope needs min side >= 240; ensure_dashscope_dims upscales if needed.
+    img = rap.ensure_dashscope_dims(image)
+    print(f"=== firing {task} layout={layout} img={img} ===", flush=True)
+    url = None; last_err = None
+    for i, key in enumerate(KEYS):
+        try:
+            gen = Wan26Generator(api_key=key, model="wan2.6-i2v-flash")
+            print(f"  trying key[{i}] ...", flush=True)
+            url = gen.generate(prompt=PROMPT_STACKED, img_path=img, duration=5)
+            break
+        except Exception as e:
+            last_err = e; print(f"  key[{i}] failed: {str(e)[:120]}", flush=True)
+    if url is None:
+        raise SystemExit(f"ALL KEYS FAILED: {last_err}")
+    dest = OUT / f"{task}_collage_{layout}.mp4"
+    rap.download_video(url, str(dest))
+    frames = OUT / f"{task}_collage_{layout}_frames"
+    try: rap.try_extract_frames(str(dest), str(frames))
+    except Exception as e: print("frame extract warn:", e)
+    print(f"SAVED {dest} ({dest.stat().st_size//1024} KB)  url={url}", flush=True)
+    with open(OUT/"results.jsonl","a") as fh:
+        fh.write(json.dumps({"task":task,"layout":layout,"video_url":url,"local":str(dest)})+"\n")
+
+if __name__ == "__main__":
+    task = sys.argv[1] if len(sys.argv)>1 else "task_0001"
+    image = sys.argv[2] if len(sys.argv)>2 else "/tmp/physical_icl/collage/task_0001_good_A_stacked.png"
+    layout = sys.argv[3] if len(sys.argv)>3 else "A"
+    run(task, image, layout)

collage_experiment/judge/task_0003_cosmos3.mp4

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+oid sha256:277095f180679ae08619abde000a5d03c1bd9cad101f71b9df231e9f5e48f01c
+size 219295

collage_experiment/judge/task_0035_cosmos3.mp4

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+oid sha256:14c3e7e7caa007c266fc3140ae579c2b50c8d91a007165ea95e6c565aee21d49
+size 529766

collage_experiment/judge/task_0065_cosmos3.mp4

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+oid sha256:9e82c9b05843f043e2767ca927492f1f45a6489e2b098dc2d640c924e07d762f
+size 282776

collage_experiment/research/basemodel_survey.md

@@ -0,0 +1,42 @@
+# ICL VideoGen Base Models — Comparison
+
+Goal: open-source, strong, supports **test video + image + mixed instruction** conditional
+video generation, usable as a **post-train base** for in-context-learning (ICL) videogen.
+
+_Compiled 2026-07-15 (each row verified against the model's repo/paper)._
+
+| Model | Foundation model | Supports test video/image/instruction (how) | Open inference | Open finetune | Released | Benchmark |
+|---|---|---|:---:|:---:|---|---|
+| **VAP** (Video-As-Prompt, ByteDance) | CogVideoX-I2V-5B / Wan2.1-I2V-14B (+5B MoT expert) | Reference **video** = semantic prompt, in-context; **frozen base + trained plug-in MoT expert** | ✅ | ✅ SFT / DPO / multi-ref | 2025-10 (ICLR'26) | user-pref 38.7% (>Kling/Vidu 38.2); VBench motion 98.59 / align 70.44 |
+| **UniVideo** (Kling Team) | HunyuanVideo + Qwen2.5-VL | MLLM parses **free-form text/image/video instruction** → MMDiT; in-context gen + edit | ✅ | ✅ (`TRAINING.md`) | arxiv 2025-10 / weights 2026-01 (ICLR'26) | **VBench-T2V 83.48**; MMBench 83.5; GenEval 0.69; ImgEdit 3.83 |
+| **LoomVideo** (PKU MSALab) | Self-built **5B DiT + MLLM** (MLLM replaces text encoder) | Interleaved: video+text / video+image+text / **multi-image+text**; Deepstack Injection + Negative-Temporal-RoPE | ✅ (`scripts/inference/generate.py`, HF wts `MSALab/LoomVideo`) | ✅ (`scripts/train` + YAML) | arxiv 2606.06042 = **2026-06-05** | VBench / OpenVE / RefVIE / GenEval / ImgEdit (SOTA claim, 5.41× speedup) |
+| **VACE** (Wan2.1-VACE, Alibaba) | Wan2.1 (14B + 1.3B) / LTX-Video 0.9 | VCU: text+image+video+mask; ref-to-video + **depth/flow control channels** | ✅ | ❌ (no official; community LoRA) | 2025-03 code / 05 weights (ICCV'25) | VACE-Benchmark (no public unified score) |
+| **VINO** | HunyuanVideo (DiT) + Qwen3VL (VLM) | **Interleaved omnimodal**: text + ref-image + ref-video → unified conditioning tokens | ✅ | ❌ (no train code) | arxiv 2026-01-05 | claims multi-bench SOTA (no single public number) |
+
+## Note on the "Benchmark" column
+There is **no single unified benchmark** all five are scored on head-to-head. **VBench** is the
+closest common framework, but each paper reports a different subset (UniVideo reports full VBench-T2V
+83.48; VAP reports only motion/align sub-scores; VACE/VINO give no directly-comparable VBench number).
+→ The column reflects each model's *own* claims and **cannot be used to rank them directly**. For a
+real apples-to-apples comparison we'd have to re-run them on one suite (e.g. VBench-I2V or our own
+physical-ICL set).
+
+## Post-train-friendliness (open inference **and** finetune)
+Only **VAP** (frozen base + trainable expert), **UniVideo**, and **LoomVideo** ship training code.
+**VACE** and **VINO** are inference-only (no official training) → post-training them means writing
+your own training loop.
+
+## Recommendation
+- **Lightest + training code + best fit for video+image+mixed instruction** → **LoomVideo** (only 5B,
+  interleaved multi-image+text, very new 2026-06). Try first.
+- **Benchmark-backed + strong mixed-instruction + full training** → **UniVideo** (heavier: HunyuanVideo base).
+- **Cheapest to start post-training** → **VAP** (freeze base, train only the expert; ships VAP-Data).
+- **Physical-control channels (depth/flow)** → **VACE** (good when you need explicit motion/physics control).
+- **VINO** — best architectural fit (unified omnimodal ICL) but **no training code**; watch for release.
+
+## Sources
+- VAP: <https://github.com/bytedance/Video-As-Prompt> (arxiv 2510.20888)
+- UniVideo: <https://github.com/KlingTeam/UniVideo> (arxiv 2510.08377)
+- LoomVideo: <https://github.com/MSALab-PKU/LoomVideo> (PKU + Alibaba, arxiv 2606.06042; HF weights `MSALab/LoomVideo`)
+- VACE: <https://github.com/ali-vilab/VACE> (ICCV 2025)
+- VINO: <https://github.com/SOTAMak1r/VINO-code> (arxiv 2601.02358)

collage_experiment/scorer/physics_iq_results.json

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+{
+  "metric": "Physics-IQ raw components (unnormalized; single real take, no real-real variance norm)",
+  "window_s": 5.0, "n_frames": 48, "size": 256, "thresh": 0.08,
+  "fov_check": "wan query-panel FOV verified aligned to GT scene on all 3 tasks (_sanity_all.png)",
+  "higher_better": ["spatial_iou","spatiotemporal_iou","weighted_spatial_iou"],
+  "lower_better": ["mse"],
+  "results": {
+    "task_0065_compress":  {"wan":{"spatial_iou":0.2821,"spatiotemporal_iou":0.2064,"weighted_spatial_iou":0.24,"mse":0.05119},
+                            "cosmos3":{"spatial_iou":0.5471,"spatiotemporal_iou":0.2037,"weighted_spatial_iou":0.6272,"mse":0.04964}},
+    "task_0003_collision": {"wan":{"spatial_iou":0.0826,"spatiotemporal_iou":0.1249,"weighted_spatial_iou":0.0773,"mse":0.01276},
+                            "cosmos3":{"spatial_iou":0.3693,"spatiotemporal_iou":0.0001,"weighted_spatial_iou":0.3792,"mse":0.00781}},
+    "task_0035_ramp":      {"wan":{"spatial_iou":0.0214,"spatiotemporal_iou":0.0309,"weighted_spatial_iou":0.0426,"mse":0.01365},
+                            "cosmos3":{"spatial_iou":0.0554,"spatiotemporal_iou":0.0223,"weighted_spatial_iou":0.2185,"mse":0.01031}}
+  },
+  "means": {"wan":{"spatial_iou":0.129,"weighted_spatial_iou":0.120},"cosmos3":{"spatial_iou":0.324,"weighted_spatial_iou":0.408}},
+  "conclusion": "wan2.6-flash collage-i2v underperforms cosmos3-Nano ICL baseline on all 3 (spatial/weighted IoU, MSE); under-produces + mislocalizes the physical motion. FOV aligned so not an artifact."
+}

collage_experiment/scorer/physics_iq_score.py

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+#!/usr/bin/env python3
+"""
+physics_iq_score.py  (CC, Physical-ICL)
+
+Reference-based Physics-IQ *component* metrics comparing a generated video's
+query panel against the real Physics-IQ GT continuation.
+
+Protocol notes (be honest about what this is):
+  * The canonical Physics-IQ Score (0-100%) normalizes each metric by the
+    real-vs-real physical variance (a 2nd real take). We only have ONE real
+    take per task here, so we report the RAW (unnormalized) components:
+      - Spatial IoU            (where motion happens, union over time)
+      - Spatiotemporal IoU     (where AND when, per-frame mask IoU, mean over t)
+      - Weighted spatial IoU   (soft IoU on continuous motion magnitude)
+      - MSE                    (pixel error inside GT motion region, lower=better)
+  * i2v conditioning = init frame only (frame 0). Full generated clip is the
+    "prediction"; compared against the matched real-time GT window.
+  * Both gen (wan) and GT are 30fps -> 5s window = 150 frames, clean match.
+    cosmos3 baseline is 24fps -> its 5s window sampled at the same timestamps.
+
+Usage:
+  python3 physics_iq_score.py <gen.mp4> <gt.mp4> [--panel] [--win 5.0] [--n 48]
+      --panel : crop bottom query panel (y>=0.495*H) from gen (collage output)
+"""
+import sys, json, subprocess, argparse
+import numpy as np
+import cv2
+
+SIZE = 256          # common working grid (stretch both to SIZE x SIZE)
+PANEL_SPLIT = 0.495 # storyboard/query split ratio (Vita: bottom ~50.5%)
+THRESH = 0.08       # binarization threshold on normalized frame-diff
+
+
+def probe(v):
+    r = subprocess.run(["ffprobe", "-v", "quiet", "-print_format", "json",
+                        "-show_streams", v], capture_output=True, text=True)
+    s = [x for x in json.loads(r.stdout)["streams"] if x["codec_type"] == "video"][0]
+    fps = eval(s["r_frame_rate"]) if "/" in s["r_frame_rate"] else float(s["r_frame_rate"])
+    return int(s["width"]), int(s["height"]), fps, float(s.get("duration") or 0)
+
+
+def load_frames(v, n, win, panel=False):
+    """Sample n frames uniformly over [0, win] seconds; optional bottom-panel crop;
+    return float array [n, SIZE, SIZE] grayscale in [0,1]."""
+    W, H, fps, dur = probe(v)
+    ts = [i * win / (n - 1) for i in range(n)]
+    cap = cv2.VideoCapture(v)
+    frames = []
+    for t in ts:
+        cap.set(cv2.CAP_PROP_POS_MSEC, t * 1000.0)
+        ok, fr = cap.read()
+        if not ok:
+            fr = np.zeros((H, W, 3), np.uint8)
+        if panel:
+            y0 = int(round(fr.shape[0] * PANEL_SPLIT))
+            fr = fr[y0:, :, :]
+        g = cv2.cvtColor(fr, cv2.COLOR_BGR2GRAY)
+        g = cv2.resize(g, (SIZE, SIZE), interpolation=cv2.INTER_AREA)
+        frames.append(g.astype(np.float32) / 255.0)
+    cap.release()
+    return np.stack(frames)  # [n,SIZE,SIZE]
+
+
+def motion(frames):
+    """Per-frame abs-diff from frame0 -> [n,SIZE,SIZE] in [0,1]."""
+    ref = frames[0]
+    return np.abs(frames - ref[None])
+
+
+def metrics(gen, gt):
+    mg, mt = motion(gen), motion(gt)          # [n,H,W]
+    bg, bt = mg > THRESH, mt > THRESH         # binary per-frame action
+
+    # Spatial IoU (union over time)
+    sg, st = bg.any(0), bt.any(0)
+    inter = np.logical_and(sg, st).sum()
+    union = np.logical_or(sg, st).sum()
+    spatial_iou = inter / union if union else 0.0
+
+    # Spatiotemporal IoU (per-frame, mean over t where either is active)
+    st_ious = []
+    for i in range(bg.shape[0]):
+        u = np.logical_or(bg[i], bt[i]).sum()
+        if u:
+            st_ious.append(np.logical_and(bg[i], bt[i]).sum() / u)
+    spatiotemporal_iou = float(np.mean(st_ious)) if st_ious else 0.0
+
+    # Weighted (soft) spatial IoU on continuous magnitude (max over time)
+    Mg, Mt = mg.max(0), mt.max(0)
+    soft_iou = float(np.minimum(Mg, Mt).sum() / max(np.maximum(Mg, Mt).sum(), 1e-8))
+
+    # MSE inside GT motion region (union over time), on gray [0,1]
+    region = st  # GT spatial action mask
+    if region.sum():
+        diff2 = (gen - gt) ** 2                # [n,H,W]
+        mse = float(diff2[:, region].mean())
+    else:
+        mse = float(((gen - gt) ** 2).mean())
+
+    return {"spatial_iou": round(spatial_iou, 4),
+            "spatiotemporal_iou": round(spatiotemporal_iou, 4),
+            "weighted_spatial_iou": round(soft_iou, 4),
+            "mse": round(mse, 5)}
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("gen"); ap.add_argument("gt")
+    ap.add_argument("--panel", action="store_true")
+    ap.add_argument("--gt-panel", action="store_true")
+    ap.add_argument("--win", type=float, default=5.0)
+    ap.add_argument("--n", type=int, default=48)
+    a = ap.parse_args()
+    gen = load_frames(a.gen, a.n, a.win, panel=a.panel)
+    gt = load_frames(a.gt, a.n, a.win, panel=a.gt_panel)
+    m = metrics(gen, gt)
+    print(json.dumps(m))
+
+
+if __name__ == "__main__":
+    main()

collage_experiment/videos/task_0003_wan26flash_480p.mp4

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+oid sha256:c53dc9b2a9db9fc471318ddaad8fab47b51ae396202624e826ad5a767071c132
+size 1054679

collage_experiment/videos/task_0035_wan26flash_480p.mp4

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+oid sha256:03e72b94ceedc76b30b1ac83625b9f95b788d6e59dd2770250cf50af3645216c
+size 1123970

collage_experiment/videos/task_0065_wan26flash_480p.mp4

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+oid sha256:42ba3f3a151e3c6f42de6ab494bb6023512a5f97a2799641b9bafc5215c5d0f1
+size 855605