Pith Number

pith:6MYRC43N

pith:2026:6MYRC43NGU6TBFE4K46YBFTVCQ

not attested not anchored not stored refs resolved

3DPhysVideo: Consistency-Guided Flow SDE for Video Generation via 3D Scene Reconstruction and Physical Simulation

Hwidong Kim, Tae-Kyun Kim, Yunho Kim

A training-free pipeline turns a single image into a physically realistic video by reconstructing 3D scenes and guiding generation with physics simulations.

arxiv:2605.16795 v1 · 2026-05-16 · cs.CV · cs.AI · cs.GR

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3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

This work introduces 3DPhysVideo, a novel training-free pipeline that generates physically realistic videos from a single image... our method successfully bridges the gap from single-images to physically plausible videos, while remaining efficient to run on a single consumer GPU. It outperforms state-of-the-art baselines on GPT-based scores, VideoPhy benchmark and human evaluation.

C2weakest assumption

The off-the-shelf image-to-video flow model can be effectively repurposed for both 3D scene reconstruction via point cloud guidance and final video synthesis via physically simulated point cloud guidance without any fine-tuning or additional training.

C3one line summary

A training-free pipeline reconstructs 3D scenes from single images, applies physics simulation, and uses Consistency-Guided Flow SDE to steer an off-the-shelf image-to-video model for physically plausible video output.

References

75 extracted · 75 resolved · 0 Pith anchors

[1] Genesis: A generative and universal physics engine for robotics and beyond, December 2024 2024

[2] Recammaster: Camera-controlled generative rendering from a single video.ICCV, 2025 2025

[3] Videophy: Evaluating physical commonsense for video generation.arXiv, 2024 2024

[4] Stable video diffusion: Scaling latent video diffusion models to large datasets.arXiv, 2023 2023

[5] Go-with-the-flow: Motion-controllable video diffusion models using real-time warped noise 2025

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:03:22.490163Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

f33111736d353d30949c573d8096751418204b488244cbc6f6ba9b975c1b8587

Aliases

arxiv: 2605.16795 · arxiv_version: 2605.16795v1 · doi: 10.48550/arxiv.2605.16795 · pith_short_12: 6MYRC43NGU6T · pith_short_16: 6MYRC43NGU6TBFE4 · pith_short_8: 6MYRC43N

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/6MYRC43NGU6TBFE4K46YBFTVCQ \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: f33111736d353d30949c573d8096751418204b488244cbc6f6ba9b975c1b8587

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "e55c3db1dfff8eb1782de943e2b093ff0716c9cfe1e7c3b26880ae828d15b085",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.GR"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-16T03:56:52Z",
    "title_canon_sha256": "4bc188e5d8a37f64ae9f63551b4a774a5cef033cff90418251ef6fdbdb149be6"
  },
  "schema_version": "1.0",
  "source": {
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    "kind": "arxiv",
    "version": 1
  }
}