192 citations · 541 across the 21 of their papers we have counts for
28 papers
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…
Generating Holistic 3D Human Motion from Speech
Hongwei Yi, Hualin Liang, Yifei Liu +5
This work addresses the problem of generating 3D holistic body motions from human speech. Given a speech recording, we synthesize sequences of 3D body poses, hand gestures, and fac…
Instant Volumetric Head Avatars
Wojciech Zielonka, Timo Bolkart, Justus Thies
We present Instant Volumetric Head Avatars (INSTA), a novel approach for reconstructing photo-realistic digital avatars instantaneously. INSTA models a dynamic neural radiance fiel…
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…
Human Body Measurement Estimation with Adversarial Augmentation
Nataniel Ruiz, Miriam Bellver, Timo Bolkart +4
We present a Body Measurement network (BMnet) for estimating 3D anthropomorphic measurements of the human body shape from silhouette images. Training of BMnet is performed on data…
Capturing and Animation of Body and Clothing from Monocular Video
Yao Feng, Jinlong Yang, Marc Pollefeys +2
While recent work has shown progress on extracting clothed 3D human avatars from a single image, video, or a set of 3D scans, several limitations remain. Most methods use a holisti…
Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation
Haiwen Feng, Timo Bolkart, Joachim Tesch +2
Virtual facial avatars will play an increasingly important role in immersive communication, games and the metaverse, and it is therefore critical that they be inclusive. This requi…
EMOCA: Emotion Driven Monocular Face Capture and Animation
Radek Danecek, Michael J. Black, Timo Bolkart
As 3D facial avatars become more widely used for communication, it is critical that they faithfully convey emotion. Unfortunately, the best recent methods that regress parametric 3…
Towards Metrical Reconstruction of Human Faces
Wojciech Zielonka, Timo Bolkart, Justus Thies
Face reconstruction and tracking is a building block of numerous applications in AR/VR, human-machine interaction, as well as medical applications. Most of these applications rely…
Learning Realistic Human Reposing using Cyclic Self-Supervision with 3D Shape, Pose, and Appearance Consistency
Soubhik Sanyal, Alex Vorobiov, Timo Bolkart +5
Synthesizing images of a person in novel poses from a single image is a highly ambiguous task. Most existing approaches require paired training images; i.e. images of the same pers…
Collaborative Regression of Expressive Bodies using Moderation
Yao Feng, Vasileios Choutas, Timo Bolkart +2
Recovering expressive humans from images is essential for understanding human behavior. Methods that estimate 3D bodies, faces, or hands have progressed significantly, yet separate…
Learning an Animatable Detailed 3D Face Model from In-The-Wild Images
Yao Feng, Haiwen Feng, Michael J. Black +1
While current monocular 3D face reconstruction methods can recover fine geometric details, they suffer several limitations. Some methods produce faces that cannot be realistically…
GIF: Generative Interpretable Faces
Partha Ghosh, Pravir Singh Gupta, Roy Uziel +3
Photo-realistic visualization and animation of expressive human faces have been a long standing challenge. 3D face modeling methods provide parametric control but generates unreali…
Monocular Expressive Body Regression through Body-Driven Attention
Vasileios Choutas, Georgios Pavlakos, Timo Bolkart +2
To understand how people look, interact, or perform tasks, we need to quickly and accurately capture their 3D body, face, and hands together from an RGB image. Most existing method…
STAR: Sparse Trained Articulated Human Body Regressor
Ahmed A. A. Osman, Timo Bolkart, Michael J. Black
The SMPL body model is widely used for the estimation, synthesis, and analysis of 3D human pose and shape. While popular, we show that SMPL has several limitations and introduce ST…
3D Morphable Face Models -- Past, Present and Future
Bernhard Egger, William A. P. Smith, Ayush Tewari +10
In this paper, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed. The challenges in building and applying these models, name…
Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
Soubhik Sanyal, Timo Bolkart, Haiwen Feng +1
The estimation of 3D face shape from a single image must be robust to variations in lighting, head pose, expression, facial hair, makeup, and occlusions. Robustness requires a larg…
Capture, Learning, and Synthesis of 3D Speaking Styles
Daniel Cudeiro, Timo Bolkart, Cassidy Laidlaw +2
Audio-driven 3D facial animation has been widely explored, but achieving realistic, human-like performance is still unsolved. This is due to the lack of available 3D datasets, mode…
Expressive Body Capture: 3D Hands, Face, and Body from a Single Image
Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani +4
To facilitate the analysis of human actions, interactions and emotions, we compute a 3D model of human body pose, hand pose, and facial expression from a single monocular image. To…
Generating 3D faces using Convolutional Mesh Autoencoders
Anurag Ranjan, Timo Bolkart, Soubhik Sanyal +1
Learned 3D representations of human faces are useful for computer vision problems such as 3D face tracking and reconstruction from images, as well as graphics applications such as…