Search Results for author:
Found 12 papers, 5 papers with code
BAGS: Building Animatable Gaussian Splatting from a Monocular Video with Diffusion Priors
In this work, we propose a method to build animatable 3D Gaussian Splatting from monocular video with diffusion priors.
A Spatial-Temporal Transformer based Framework For Human Pose Assessment And Correction in Education Scenarios
Human pose assessment and correction play a crucial role in applications across various fields, including computer vision, robotics, sports analysis, healthcare, and entertainment.
MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations
In this work, we present MoConVQ, a novel unified framework for physics-based motion control leveraging scalable discrete representations.
Robust Dancer: Long-term 3D Dance Synthesis Using Unpaired Data
How to automatically synthesize natural-looking dance movements based on a piece of music is an incrementally popular yet challenging task.
GestureDiffuCLIP: Gesture Diffusion Model with CLIP Latents
We leverage the power of the large-scale Contrastive-Language-Image-Pre-training (CLIP) model and present a novel CLIP-guided mechanism that extracts efficient style representations from multiple input modalities, such as a piece of text, an example motion clip, or a video.
Moby: Empowering 2D Models for Efficient Point Cloud Analytics on the Edge
3D object detection plays a pivotal role in many applications, most notably autonomous driving and robotics.
MotionBERT: A Unified Perspective on Learning Human Motion Representations
We present a unified perspective on tackling various human-centric video tasks by learning human motion representations from large-scale and heterogeneous data resources.
Ranked #1 on
Monocular 3D Human Pose Estimation
on Human3.6M
(using extra training data)
ControlVAE: Model-Based Learning of Generative Controllers for Physics-Based Characters
Our framework can learn a rich and flexible latent representation of skills and a skill-conditioned generative control policy from a diverse set of unorganized motion sequences, which enables the generation of realistic human behaviors by sampling in the latent space and allows high-level control policies to reuse the learned skills to accomplish a variety of downstream tasks.
Rhythmic Gesticulator: Rhythm-Aware Co-Speech Gesture Synthesis with Hierarchical Neural Embeddings
We present a novel co-speech gesture synthesis method that achieves convincing results both on the rhythm and semantics.
Ranked #2 on
Gesture Generation
on TED Gesture Dataset
Neural Novel Actor: Learning a Generalized Animatable Neural Representation for Human Actors
The learned representation can be used to synthesize novel view images of an arbitrary person from a sparse set of cameras, and further animate them with the user's pose control.
Unsupervised Co-part Segmentation through Assembly
Co-part segmentation is an important problem in computer vision for its rich applications.
Learning Skeletal Articulations with Neural Blend Shapes
Furthermore, we propose neural blend shapes--a set of corrective pose-dependent shapes which improve the deformation quality in the joint regions in order to address the notorious artifacts resulting from standard rigging and skinning.
