TL;DR
ProJo4D introduces a progressive joint optimization framework that improves physical parameter estimation and 4D future state prediction from sparse-view visual data, outperforming prior methods.
Contribution
It presents a novel progressive joint optimization approach that stabilizes physics-aware neural rendering in sparse-view scenarios, enabling scalable and accurate inverse physics estimation.
Findings
Up to 10x improvement in geometric accuracy.
Significant enhancement in physical parameter estimation.
Effective in both synthetic and real-world datasets.
Abstract
Neural rendering has advanced significantly in 3D reconstruction and novel view synthesis, and integrating physics into these frameworks opens new applications such as physically accurate digital twins for robotics and XR. However, the inverse problem of estimating physical parameters from visual observations remains challenging. Existing physics-aware neural rendering methods typically require dense multi-view videos, making them impractical for scalable, real-world deployment. Under sparse-view settings, the sequential optimization strategies employed by current approaches suffer from severe error accumulation: inaccuracies in initial 3D reconstruction propagate to subsequent stages, degrading physical state and material parameter estimates. On the other hand, simultaneous optimization of all parameters fails due to the highly non-convex and often non-differentiable nature of the…
Peer Reviews
Decision·Submitted to ICLR 2026
1. Innovative Progressive Joint Optimization Paradigm Solves Core Sparse-View Challenges. ProJo4D addresses the two critical bottlenecks of inverse physics estimation under sparse views: error accumulation in sequential optimization and trapping in local minima in full joint optimization, by proposing a stage-wise variable expansion strategy. 2. End-to-End Integration of 4D Dynamic Representation and Differentiable Physics Simulation. The framework tightly couples 3D Gaussian Splatting-based 4
1. The paper does not report key efficiency indicators such as per-frame optimization time or GPU memory usage. 2. It requires manual designation of material types and cannot handle unknown or mixed materials, limiting applicability to real-world scenes. 3. Experiments focus on 3-view settings, with no results for 2-view or single-view scenarios (common in real-world robotic monocular observation). It is unclear whether ProJo4D can maintain accuracy when view count drops further. 4. The "low-sen
1. The paper expresses its main target very clearly, making it easy to follow 2. Although the method is simple, the improvement is obvious.
Although the method shows great performance in the experiments, there are still some conceptual or experimental weaknesses: 1. Since the stage 0 is purely learned with RGB supervision with no other constraints, it is possible that the learned deformation field does not obey the physical smoothness. Once the deformation field is learned, it will not be optimized further. So if there is issue in the first stage, the issue will accumulate to the later stages. The influence of enabling further refin
1. The presentation is clear, and the background is investigated comprehensively. 2. The optimization design of ProJo4D framework is neat overall.
1. The Deformation Network is only optimized at stage 0, which may not be correct. The later estimation for initial velocity and material parameters through physics simulation will rely on the predicted positions from the Deformation Network, which may not be reliable. 2. It is unclear what the key is to the optimization strategy of ProJo4D. Is it the progressive estimation for different kinds of parameters? Or is it due to some parameters are optimized repeatedly across multiple stages? More
Code & Models
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Taxonomy
Topics3D Shape Modeling and Analysis · Generative Adversarial Networks and Image Synthesis · Model Reduction and Neural Networks
MethodsSparse Evolutionary Training
