Open Problems

3D Gaussian Splatting & Novel View Synthesis

Dynamic 3D Gaussian Splatting Under Non-Smooth and Discontinuous Motion Dynamics

Effect to explainPartly addressed
Possible candidate · 3/5 runs4 papers report this75% from 2025+

Generated automatically from the limitations stated in 4 papers (ICLR, CVPR), listed under Evidence. It is not a paper, and it does not come from papers submitted to CSPaper.

The problem

Current dynamic novel view synthesis and 4D Gaussian Splatting frameworks assume smooth, continuous spatio-temporal deformations, linear velocities, and fixed temporal windows. When scenes exhibit abrupt impacts, rapid accelerations, chaotic object trajectories, or zooming and variable frame rates, these continuous deformation priors fail and yield severe geometric tearing, blurring, or total tracking collapse. This fundamentally blocks dynamic radiance fields from capturing real-world interactions such as sports action, mechanical impacts, and sudden dynamic occlusions.

Why it matters

Enables robust free-viewpoint rendering and 4D scene reconstruction for chaotic, fast-action, and discontinuous dynamic phenomena without requiring slow-motion capture or artificial motion smoothing.

Ways to approach it

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  1. 1

    Piecewise and keyframe-anchored trajectory parameterization: Replace global low-frequency continuous MLPs or polynomials with adaptive piecewise splines that detect and fit trajectory inflection points, measuring novel-view PSNR and LPIPS on benchmark sequences containing sudden motion transitions.

  2. 2

    Impulse- and acceleration-aware trajectory regularizers: Integrate higher-order temporal derivatives and change-point detection into the Gaussian deformation optimization pipeline, measuring rendering fidelity and point drift during abrupt stops, rebounds, and directional reversals.

  3. 3

    Hybrid multi-rate / asynchronous temporal anchoring: Ingest variable-rate frame inputs or auxiliary motion signals (e.g., optical flow discontinuities or event camera streams) to constrain Gaussian positions across sharp transitions, measuring reconstruction accuracy across variable-fps captures.

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Why it might fail

Severe motion blur in standard camera captures during sudden dynamic transitions may destroy the high-frequency visual features needed to initialize and position Gaussians accurately, regardless of how expressive the temporal trajectory formulation is.

Evidence

Each paper's own statement of the limitation, verbatim.

Nearest existing work

Related open problems

3D Gaussian Splatting & Novel View Synthesis

Scope to testOpen

Cross-Domain Robustness and Generalization Benchmarking for 3D Gaussian Splatting

Novel view synthesis and 3D Gaussian Splatting methods are currently evaluated almost exclusively on room-scale indoor datasets such as Replica, ScanNet++, and Gibson. Because no single pipeline has been systematically evaluated across indoor, outdoor unbounded, panoramic, and object-centric domains, the robustness and failure modes of these representations under domain shift remain entirely unknown. Practitioners and researchers cannot determine whether observed performance gains are artifacts of bounded indoor geometries or if current densification and optimization heuristics translate to unconstrained environments.

Possible candidate · 2/5 runs7 papers report this71% from 2025+

3D Gaussian Splatting & Novel View Synthesis

Barrier to removeOpen

Robust Wide-Baseline 3D Gaussian Splatting Under Unreliable Foundation-Model Geometry Priors

Modern feed-forward 3D Gaussian Splatting pipelines rely on pairwise foundation models (such as MASt3R or VGGT) to supply initial coordinate pointmaps and relative camera poses. Because these foundation predictors degrade significantly as angular view differences increase and inject persistent geometric noise, downstream view synthesis fails when views are not captured in dense, small-step sequences. Treating these imperfect upstream predictions as rigid pseudo-ground truth structurally blocks feed-forward novel view synthesis on sparse, wide-baseline image collections.

Weak candidate · 1/3 runs3 papers report this100% from 2025+
Generated automatically, not curated by hand. Automated prior-work checks catch about a third of existing work, so treat this problem as a lead to investigate.