Back to fromdesigner Some thoughts on bridging the gap in problem-solving
World Tracing borrows a ray-centric formulation to invert the image formation process, transforming pixel observations into structured geometric inference along camera rays. It is primarily designed for single-view 2D-to-3D reconstruction, a fundamentally underconstrained problem in which large portions of scene geometry are not directly observable and must be inferred through learned priors. As a result, performance is heavily dependent on the quality and completeness of input observations, with degradation arising from factors such as overexposure, missing regions, or other forms of corrupted visual evidence. In this setting, controllability and reliability of prior-induced bias become critical concerns, particularly in real-world applications where reconstruction errors can propagate into downstream tasks.
MoFo can be understood as a diffusion-based spatial generative model operating over a unified RGB–D representation space. It jointly models RGB and depth as coupled modalities, effectively learning a shared latent space in which appearance and geometry are co-dependent rather than separately inferred. The idea that humans automatically infer RGB-D is a very strong approximate solution (also to me). Nevertheless, current systems still lack a fully grounded, action-conditioned (time and action) causal state representation. More specifically: diffusion models excel at distributional generation and uncertainty modeling, but do not inherently encode causal structure. RGB-D representations impose useful geometric constraints, yet often struggle in highly dynamic, interactive, or non-rigid environments. MoFo-style systems improve geometric consistency and controllability, but they still rely on learned observational priors rather than intervention-based causal grounding.
An interesting contrast emerges in how these systems are constructed. Traditional computer graphics and reconstruction pipelines are often framed as the recovery of a coherent geometric signal from noisy observations. Diffusion-based generative models operate in the opposite direction: they deliberately corrupt data with noise and then learn to progressively denoise it back into structured representations. While both paradigms involve denoising, they address fundamentally different inverse problems.
Flex4DHuman performs a pipeline of monocular (or sparse-view) video → synchronized multi-view video → dynamic 3D human reconstruction (4D Human). By leveraging large-scale video data, it learns a geometry-consistent observational world prior rather than an intervention-based causal model. Consequently, Flex4DHuman neither solves causality nor entirely bypasses it. Instead, it occupies an intermediate regime in which observational geometry and motion priors implicitly encode weak causal regularities, while explicit intervention-level reasoning remains absent.
The adoption of 4D Gaussian Splatting substantially improves the optimization landscape, rendering efficiency and the feasibility of local updates. A key implication is that local updates become possible. Unlike scene representations such as NeRFs, implicit surfaces, or traditional 3D meshes, which often require costly global optimization or near-full retraining when new observations arrive, 4D Gaussian representations can update localized regions more efficiently. This advantage is particularly important for dynamic scenes. Many practitioners still remember the limitations of mesh-based optimization: reconstruction artifacts, topology constraints, and slow temporal updates. In comparison, 4D Gaussian Splatting provides a more flexible and scalable representation for continuously refining scenes.
It is worth noting, however, that the optimization and local-update benefits brought by 4D Gaussian Splatting, together with the temporal modeling advantages demonstrated by Flex4DHuman, do not fundamentally solve one of the remaining bottlenecks of generative scene modeling: spurious dynamics (or hallucinated dynamics). Models may still generate plausible but physically incorrect motions, infer unseen dynamics that never occurred, or maintain temporal consistency without learning the true underlying causal mechanisms governing scene evolution.
In the AI era, we increasingly rely on and embrace machine learning systems, where training objectives can be defined across a wide range of scales and formulations. Personally, if a learning system consistently maintains the same conclusion despite strong counterevidence, the key issue may not be its ability to learn, but its ability to update beliefs. Possible causes include limitations in the objective function, training data, alignment mechanisms, or the belief-updating process.
While training termination can freeze existing behaviors, persistent failures may also reflect spurious correlations, distribution shift, or objectives that favor consistency over causal understanding. The challenge, therefore, is not merely learning more data, but learning how to revise hypotheses in light of new evidence.