most citedCASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters

39 citations · 39 across the 3 of their papers we have counts for

collaborators

8 papers

cs.RO2024

Language-Augmented Symbolic Planner for Open-World Task Planning

Guanqi Chen, Lei Yang, Ruixing Jia +5

Enabling robotic agents to perform complex long-horizon tasks has been a long-standing goal in robotics and artificial intelligence (AI). Despite the potential shown by large langu…

cs.CV2024

On Optimal Sampling for Learning SDF Using MLPs Equipped with Positional Encoding

Guying Lin, Lei Yang, Yuan Liu +6

Neural implicit fields, such as the neural signed distance field (SDF) of a shape, have emerged as a powerful representation for many applications, e.g., encoding a 3D shape and pe…

cs.CV2023

Generative Hierarchical Temporal Transformer for Hand Pose and Action Modeling

Yilin Wen, Hao Pan, Takehiko Ohkawa +5

We present a novel unified framework that concurrently tackles recognition and future prediction for human hand pose and action modeling. Previous works generally provide isolated…

cs.GR202339 cited

CASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters

Zhiyang Dou, Xuelin Chen, Qingnan Fan +2

We present CASE, an efficient and effective framework that learns conditional Adversarial Skill Embeddings for physics-based characters. Our physically simulated character c…

cs.GR2023

Neural Parametric Surfaces for Shape Modeling

Lei Yang, Yongqing Liang, Xin Li +6

The recent surge of utilizing deep neural networks for geometric processing and shape modeling has opened up exciting avenues. However, there is a conspicuous lack of research effo…

cs.GR2023

Surface Extraction from Neural Unsigned Distance Fields

Congyi Zhang, Guying Lin, Lei Yang +5

We propose a method, named DualMesh-UDF, to extract a surface from unsigned distance functions (UDFs), encoded by neural networks, or neural UDFs. Neural UDFs are becoming increasi…