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Found 14 papers, 6 papers with code
Graph Canvas for Controllable 3D Scene Generation
Spatial intelligence is foundational to AI systems that interact with the physical world, particularly in 3D scene generation and spatial comprehension.
Semantic Gesticulator: Semantics-Aware Co-Speech Gesture Synthesis
In this work, we present Semantic Gesticulator, a novel framework designed to synthesize realistic gestures accompanying speech with strong semantic correspondence.
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.
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.
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)
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.
