192 citations · 319 across the 11 of their papers we have counts for
20 papers · 1 filter
Emotional Speech-driven 3D Body Animation via Disentangled Latent Diffusion
Kiran Chhatre, Radek Daněček, Nikos Athanasiou +4
Existing methods for synthesizing 3D human gestures from speech have shown promising results, but they do not explicitly model the impact of emotions on the generated gestures. Ins…
Learning Disentangled Avatars with Hybrid 3D Representations
Yao Feng, Weiyang Liu, Timo Bolkart +3
Tremendous efforts have been made to learn animatable and photorealistic human avatars. Towards this end, both explicit and implicit 3D representations are heavily studied for a ho…
SCULPT: Shape-Conditioned Unpaired Learning of Pose-dependent Clothed and Textured Human Meshes
Soubhik Sanyal, Partha Ghosh, Jinlong Yang +3
We present SCULPT, a novel 3D generative model for clothed and textured 3D meshes of humans. Specifically, we devise a deep neural network that learns to represent the geometry and…
Instant Multi-View Head Capture through Learnable Registration
Timo Bolkart, Tianye Li, Michael J. Black
Existing methods for capturing datasets of 3D heads in dense semantic correspondence are slow, and commonly address the problem in two separate steps; multi-view stereo (MVS) recon…
Emotional Speech-Driven Animation with Content-Emotion Disentanglement
Radek Daněček, Kiran Chhatre, Shashank Tripathi +3
To be widely adopted, 3D facial avatars must be animated easily, realistically, and directly from speech signals. While the best recent methods generate 3D animations that are sync…
SUPR: A Sparse Unified Part-Based Human Representation
Ahmed A. A. Osman, Timo Bolkart, Dimitrios Tzionas +1
Statistical 3D shape models of the head, hands, and fullbody are widely used in computer vision and graphics. Despite their wide use, we show that existing models of the head and h…