ShadowDancer: Teaching Video World Models Any Action by Learning Unified Dynamics Representations from a Video and Its Shadow

• URL: http://arxiv.org/abs/2607.28362v1
• Date: Thu, 30 Jul 2026 15:28:43 GMT
• Title: ShadowDancer: Teaching Video World Models Any Action by Learning Unified Dynamics Representations from a Video and Its Shadow
• Abstract summary: We present ShadowDancer, a novel approach to any-action, frame-level control of interactive video world models.<n>Existing interfaces either encode an action loosely, leaving how it unfolds for the model to improvise, or encode it exactly through structured signals.<n>ShadowDancer addresses this with two key innovations: shadow pairs, video pairs that replay the same dynamics under independently resampled appearance, and cross-shadow prediction, which learns actions by predicting one shadow from the other.
• Score: 24.623235518370695
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present ShadowDancer, a novel approach to any-action, frame-level control of interactive video world models. The obstacle is representational: existing interfaces either encode an action loosely, leaving how it unfolds for the model to improvise, or encode it exactly through structured signals that serve one family and are hard to acquire, so precise control across diverse dynamics remains impractical. Demonstration videos are the natural remedy, specifying any dynamics frame by frame; yet a video shows its dynamics only through one particular appearance, a single shadow of the underlying dynamics, so actions learned from demonstrations transfer poorly to new scenes. ShadowDancer addresses this with two key innovations: (1) shadow pairs, video pairs that replay the same dynamics under independently resampled appearance, constructed at scale by our Shadow Library, so that a dynamics family becomes controllable exactly when such pairs can be constructed for it; and (2) cross-shadow prediction, which learns actions by predicting one shadow from the other, so that whatever the pairing resamples is discarded by construction and whatever it preserves becomes the action, yielding a unified dynamics representation that drives a block-causal world model. Any demonstrated clip thus becomes a reusable action asset, replayed in new environments without action labels, motion estimators, or fine-tuning. Experiments demonstrate improved action transfer and long action rollout over strong latent-action and interactive world model baselines across diverse dynamics families, with an average blinded win rate of 86% in rollout comparisons. We show video results at https://ShadowDancer-1.github.io

Related papers

• Self-Supervised Learning of Structured Dynamics from Videos [76.23834498074805]
  We study whether structured motion representations can be recovered from frozen features of a pretrained image vision transformer.<n>We propose the Structured Dynamics Model (SDM), which explicitly separates the dominant source of temporal change from residual dynamics.<n>We evaluate SDM on ProbeMotion, a new evaluation suite spanning synthetic and real videos with camera motion, object motion, and combined dynamics.
  arXiv  Detail & Related papers  (2026-07-23T17:55:07Z)
• ActionParty: Multi-Subject Action Binding in Generative Video Games [117.52562594944679]
  ActionParty is a controllable multi-subject world model for generative video games.<n>We demonstrate the first video world model capable of controlling up to seven players simultaneously across 46 diverse environments.
  arXiv  Detail & Related papers  (2026-04-02T17:59:58Z)
• DynaVid: Learning to Generate Highly Dynamic Videos using Synthetic Motion Data [51.316274891736164]
  DynaVid is a video synthesis framework that leverages synthetic motion data in training.<n>We show that DynaVid improves the realism and controllability in dynamic motion generation and camera motion control.
  arXiv  Detail & Related papers  (2026-04-02T06:12:38Z)
• Dexterous World Models [24.21588354488453]
  Dexterous World Model (DWM) is a scene-action-conditioned video diffusion framework.<n>We show how DWM generates temporally coherent videos depicting plausible human-scene interactions.<n>Experiments demonstrate that DWM enables realistic and physically plausible interactions, such as grasping, opening, and moving objects.
  arXiv  Detail & Related papers  (2025-12-19T18:59:51Z)
• SynMotion: Semantic-Visual Adaptation for Motion Customized Video Generation [56.90807453045657]
  SynMotion is a motion-customized video generation model that jointly leverages semantic guidance and visual adaptation.<n>At the semantic level, we introduce the dual-em semantic comprehension mechanism which disentangles subject and motion representations.<n>At the visual level, we integrate efficient motion adapters into a pre-trained video generation model to enhance motion fidelity and temporal coherence.
  arXiv  Detail & Related papers  (2025-06-30T10:09:32Z)
• Pre-Trained Video Generative Models as World Simulators [59.546627730477454]
  We propose Dynamic World Simulation (DWS) to transform pre-trained video generative models into controllable world simulators.<n>To achieve precise alignment between conditioned actions and generated visual changes, we introduce a lightweight, universal action-conditioned module.<n> Experiments demonstrate that DWS can be versatilely applied to both diffusion and autoregressive transformer models.
  arXiv  Detail & Related papers  (2025-02-10T14:49:09Z)
• Video Creation by Demonstration [59.389591010842636]
  We present $delta$-Diffusion, a self-supervised training approach that learns from unlabeled videos by conditional future frame prediction.<n>By leveraging a video foundation model with an appearance bottleneck design on top, we extract action latents from demonstration videos for conditioning the generation process.<n> Empirically, $delta$-Diffusion outperforms related baselines in terms of both human preference and large-scale machine evaluations.
  arXiv  Detail & Related papers  (2024-12-12T18:41:20Z)
• Puppet-Master: Scaling Interactive Video Generation as a Motion Prior for Part-Level Dynamics [79.4785166021062]
  We introduce Puppet-Master, an interactive video generator that captures the internal, part-level motion of objects.<n>We demonstrate that Puppet-Master learns to generate part-level motions, unlike other motion-conditioned video generators.<n>Puppet-Master generalizes well to out-of-domain real images, outperforming existing methods on real-world benchmarks.
  arXiv  Detail & Related papers  (2024-08-08T17:59:38Z)
• Learn the Force We Can: Enabling Sparse Motion Control in Multi-Object Video Generation [26.292052071093945]
  We propose an unsupervised method to generate videos from a single frame and a sparse motion input. Our trained model can generate unseen realistic object-to-object interactions. We show that YODA is on par with or better than state of the art video generation prior work in terms of both controllability and video quality.
  arXiv  Detail & Related papers  (2023-06-06T19:50:02Z)
• STaR: Self-supervised Tracking and Reconstruction of Rigid Objects in Motion with Neural Rendering [9.600908665766465]
  We present STaR, a novel method that performs Self-supervised Tracking and Reconstruction of dynamic scenes with rigid motion from multi-view RGB videos without any manual annotation. We show that our method can render photorealistic novel views, where novelty is measured on both spatial and temporal axes.
  arXiv  Detail & Related papers  (2020-12-22T23:45:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.