11 accepted papers
Recent advances in motion diffusion models have substantially improved the realism of human motion synthesis. However, existing approaches fall into two categories: full-sequence diffusion models with bidirectional generation, which limit temporal causality and real-time applicability, and autoregre
Generating human motion with precise spatial control is a challenging problem. Existing approaches often require task-specific training or slow optimization, and enforcing hard constraints frequently disrupts motion naturalness. Building on the observation that many animation tasks can be formulated
Generating realistic group interactions involving multiple characters remains challenging due to increasing complexity as group size expands. While existing conditional diffusion models incrementally generate motions by conditioning on previously generated characters, they rely on single shared prom…
Generation of 3D human motion holds significant importance in the creative industry. While recent notable advances have been made in generating common motions, existing methods struggle to generate diverse and rare motions due to the complexity of motions and limited training data. This work introdu…
To build a cross-modal latent space between 3D human motion and language acquiring large-scale and high-quality human motion data is crucial. However unlike the abundance of image data the scarcity of motion data has limited the performance of existing motion-language models. To counter this we intr…
In recent years, skeleton-based action recognition has achieved remarkable performance in understanding human motion from sequences of skeleton data, which is an important medium for synthesizing realistic human movement in various applications. However, existing methods assume that each action clip…
This research tackles the problem of generating interaction between two human actors corresponding to textual description. We claim that certain interactions, which we call asymmetric interactions, involve a relationship between an actor and a receiver, whose motions significantly differ depending o…
We consider the deep point cloud analysis tasks where the inputs of the networks are randomly rotated. Recent progress in rotation-invariant point cloud analysis is mainly driven by converting point clouds into their respective canonical poses, and principal component analysis (PCA) is a practical t…
We consider the shuffled linear regression problem where the correspondences between covariates and responses are unknown. While the existing formulation assumes an ideal underlying bijection in which all pieces of data should match, such an assumption barely holds in real-world applications due to…
Point set registration plays a critical role in robotics and computer vision. Early methods considered registration as a purely geometric problem, presenting excellent extensibility for various tasks due to their explicit handling of correspondences; statistical methods were later introduced to hand…
We present a novel representation for point clouds that encapsulates the local characteristics of the underlying structure. The key idea is to embed an implicit representation of the point cloud, namely the distance field, into neural networks. One neural network is used to embed a portion of the di…