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Suggesting Variable Order for Cylindrical Algebraic Decomposition via Reinforcement Learning
The other is GRL-SVO(NUP), a fast heuristic providing a total order directly.
Multi-body SE(3) Equivariance for Unsupervised Rigid Segmentation and Motion Estimation (Supplementary Material)
Differently, our unsupervised multi-body task requires the model's ability to handle part-level local equivariance, Figure 1: Structure of our feature extractor based on EPN. "EPNConv" is the SE(3)-equivariant convolution proposed in the vanilla EPN network. Part-level SE(3)-equivariance is desirable for motion analysis, especially rotation estimation. Song and Y ang utilized the methodology proposed by Choy et al . All other objects were considered part of the background.
Multi-body SE(3) Equivariance for Unsupervised Rigid Segmentation and Motion Estimation
A truly generalizable approach to rigid segmentation and motion estimation is fundamental to 3D understanding of articulated objects and moving scenes.
NA VI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotations
Recent advances in neural reconstruction enable high-quality 3D object reconstruction from casually captured image collections. Current techniques mostly analyze their progress on relatively simple image collections where Structure-from-Motion (SfM) techniques can provide ground-truth (GT) camera poses.
InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint Zhenzhi Wang
Text-conditioned motion synthesis has made remarkable progress with the emergence of diffusion models. However, the majority of these motion diffusion models are primarily designed for a single character and overlook multi-human interactions.
Reinforcement learning from Human Feedback (RLHF) learns from preference signals, while standard Reinforcement Learning (RL) directly learns from reward
The latter case can be further reduced to adversarial MDP when preferences only depend on the final state.