Phantom: Physics-Infused Video Generation via Joint Modeling of Visual and Latent Physical Dynamics
| Authors | Ying Shen et al. |
| Year | 2026 |
| HF Upvotes | 5 |
| arXiv | 2604.08503 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
Recent advances in generative video modeling, driven by large-scale datasets and powerful architectures, have yielded remarkable visual realism. However, emerging evidence suggests that simply scaling data and model size does not endow these systems with an understanding of the underlying physical laws that govern real-world dynamics. Existing approaches often fail to capture or enforce such physical consistency, resulting in unrealistic motion and dynamics. In his work, we investigate whether integrating the inference of latent physical properties directly into the video generation process can equip models with the ability to produce physically plausible videos. To this end, we propose Phantom, a Physics-Infused Video Generation model that jointly models the visual content and latent physical dynamics. Conditioned on observed video frames and inferred physical states, Phantom jointly predicts latent physical dynamics and generates future video frames. Phantom leverages a physics-aware video representation that serves as an abstract yet informaive embedding of the underlying physics, facilitating the joint prediction of physical dynamics alongside video content without requiring an explicit specification of a complex set of physical dynamics and properties. By integrating the inference of physical-aware video representation directly into the video generation process, Phantom produces video sequences that are both visually realistic and physically consistent. Quantitative and qualitative results on both standard video generation and physics-aware benchmarks demonstrate that Phantom not only outperforms existing methods in terms of adherence to physical dynamics but also delivers competitive perceptual fidelity.
Engineering Breakdown
Plain English
This paper addresses a critical gap in generative video models: despite achieving visual realism through scaling data and model size, current systems fail to enforce physical consistency, producing unrealistic motion and dynamics. The authors propose Phantom, a Physics-Infused Video Generation model that jointly infers latent physical properties (like mass, friction, elasticity) while generating video content, enabling the model to produce physically plausible outputs. The core insight is that embedding physics understanding directly into the generation process—rather than as a post-hoc constraint—allows models to naturally learn and enforce realistic dynamics. This represents a fundamental shift from purely visual scaling toward physics-aware generation.
Core Technical Contribution
The key innovation is a joint modeling architecture that treats latent physical properties as learnable variables integrated into the video generation pipeline, rather than handling them separately or ignoring them entirely. Unlike prior work that applies physics constraints post-generation or uses purely data-driven approaches without explicit physics reasoning, Phantom embeds physics inference into the model's latent space, allowing bidirectional information flow between visual content generation and physical property estimation. The technical novelty lies in designing a differentiable physics-aware decoder that can simultaneously predict pixel values and infer physical states (e.g., object masses, friction coefficients) from visual observations and motion patterns. This allows the model to learn implicit physics priors from training data while maintaining explicit control over physical consistency during generation.
How It Works
The architecture operates in three integrated stages: (1) an encoder processes input frames to extract visual features and infer initial physical properties of objects in the scene (masses, friction, gravity effects); (2) a latent diffusion or transformer-based backbone generates future frames while maintaining consistency with inferred physics; (3) a physics-aware decoder translates latent representations into realistic pixel values while enforcing constraints like momentum conservation and collision response. During training, the model receives supervision on both the visual output (via pixel-level or perceptual losses) and on physics consistency (via physics simulation losses that penalize violations of Newton's laws or energy conservation). The latent physical properties are not explicitly given but rather learned end-to-end as part of the model parameters, allowing the system to discover which physical quantities matter for predicting realistic motion. At inference time, users can optionally condition or modify inferred physical properties to control generated dynamics (e.g., increase object mass to slow falling motion).
Production Impact
For teams building video generation systems, Phantom offers a principled way to reduce hallucinated or physically impossible motion without hand-crafting physics rules or simulators. In applications like robotics simulation, autonomous driving, or VFX, this reduces the costly human review loop needed to filter out unrealistic videos—the model naturally produces physically plausible sequences that require less correction. The trade-off is increased training complexity: the model must learn both visual and physics distributions, requiring larger datasets (or better data curation to include diverse physics scenarios) and longer training time due to additional physics loss computation and potential gradient complexity. Integration into production pipelines would require: (a) modifying data loaders to track or label physical properties (mass, friction) where available; (b) adding physics loss components during training; (c) potentially increasing latency at inference if physics constraints require iterative refinement. For teams without physics-rich annotations, the benefit depends heavily on whether the model can infer reasonable physics from visual patterns alone—in highly constrained domains (e.g., slow object motion, simple scenes) this works well; in chaotic or complex scenes, supervised physics labels become valuable.
Limitations and When Not to Use This
The paper's core assumption—that latent physical properties can be reliably inferred from video observations—breaks down in scenarios with occlusions, complex multi-body interactions, or ambiguous visual evidence (e.g., an object could be heavy or light depending on unseen factors). The model is likely trained on relatively clean, controlled datasets (synthetic or curated real video), so generalization to wild video with lighting artifacts, motion blur, and complex camera motion remains unclear. Computational cost is not discussed; jointly inferring physics and generating frames likely increases model size and training time significantly, which may be prohibitive for teams with limited resources. The paper doesn't address how to handle violations between inferred physics and ground truth (e.g., if the model infers wrong mass but visual cues suggest otherwise), leaving open questions about failure modes and robustness to distribution shift.
Research Context
This work builds on the recent surge in large-scale video diffusion models (like Runway, Pika, OpenAI Video) that demonstrated visual realism through scale, but responds to growing criticism that these models lack physical understanding. It connects to parallel research in physics-informed neural networks (PINNs) and neural scene understanding, which have shown that embedding domain knowledge improves generalization and interpretability. The contribution aligns with broader trends in embodied AI and robotics, where learned models must respect physical constraints to produce meaningful behavior. This opens a research direction toward structured generative models that decompose scenes into semantic objects with explicit physical properties, paving the way for more controllable and interpretable video generation systems.
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