Search Results for author: Andrei Zanfir

Found 17 papers, 0 papers with code

VLOGGER: Multimodal Diffusion for Embodied Avatar Synthesis

no code implementations13 Mar 2024 Enric Corona, Andrei Zanfir, Eduard Gabriel Bazavan, Nikos Kolotouros, Thiemo Alldieck, Cristian Sminchisescu

We propose VLOGGER, a method for audio-driven human video generation from a single input image of a person, which builds on the success of recent generative diffusion models.

Face Detection Video Editing +1

DiffHuman: Probabilistic Photorealistic 3D Reconstruction of Humans

no code implementations CVPR 2024 Akash Sengupta, Thiemo Alldieck, Nikos Kolotouros, Enric Corona, Andrei Zanfir, Cristian Sminchisescu

Our experiments show that DiffHuman can produce diverse and detailed reconstructions for the parts of the person that are unseen or uncertain in the input image while remaining competitive with the state-of-the-art when reconstructing visible surfaces.

3D Human Reconstruction 3D Reconstruction +2

SPHEAR: Spherical Head Registration for Complete Statistical 3D Modeling

no code implementations4 Nov 2023 Eduard Gabriel Bazavan, Andrei Zanfir, Thiemo Alldieck, Teodor Alexandru Szente, Mihai Zanfir, Cristian Sminchisescu

We present \emph{SPHEAR}, an accurate, differentiable parametric statistical 3D human head model, enabled by a novel 3D registration method based on spherical embeddings.

Blendshapes GHUM: Real-time Monocular Facial Blendshape Prediction

no code implementations11 Sep 2023 Ivan Grishchenko, Geng Yan, Eduard Gabriel Bazavan, Andrei Zanfir, Nikolai Chinaev, Karthik Raveendran, Matthias Grundmann, Cristian Sminchisescu

We present Blendshapes GHUM, an on-device ML pipeline that predicts 52 facial blendshape coefficients at 30+ FPS on modern mobile phones, from a single monocular RGB image and enables facial motion capture applications like virtual avatars.

Structured 3D Features for Reconstructing Controllable Avatars

no code implementations CVPR 2023 Enric Corona, Mihai Zanfir, Thiemo Alldieck, Eduard Gabriel Bazavan, Andrei Zanfir, Cristian Sminchisescu

We introduce Structured 3D Features, a model based on a novel implicit 3D representation that pools pixel-aligned image features onto dense 3D points sampled from a parametric, statistical human mesh surface.

3D Reconstruction Novel View Synthesis +1

BlazePose GHUM Holistic: Real-time 3D Human Landmarks and Pose Estimation

no code implementations23 Jun 2022 Ivan Grishchenko, Valentin Bazarevsky, Andrei Zanfir, Eduard Gabriel Bazavan, Mihai Zanfir, Richard Yee, Karthik Raveendran, Matsvei Zhdanovich, Matthias Grundmann, Cristian Sminchisescu

We present BlazePose GHUM Holistic, a lightweight neural network pipeline for 3D human body landmarks and pose estimation, specifically tailored to real-time on-device inference.

3D Human Pose Estimation

HSPACE: Synthetic Parametric Humans Animated in Complex Environments

no code implementations23 Dec 2021 Eduard Gabriel Bazavan, Andrei Zanfir, Mihai Zanfir, William T. Freeman, Rahul Sukthankar, Cristian Sminchisescu

We combine a hundred diverse individuals of varying ages, gender, proportions, and ethnicity, with hundreds of motions and scenes, as well as parametric variations in body shape (for a total of 1, 600 different humans), in order to generate an initial dataset of over 1 million frames.

3D Human Pose Estimation Scene Understanding

Weakly Supervised 3D Human Pose and Shape Reconstruction with Normalizing Flows

no code implementations ECCV 2020 Andrei Zanfir, Eduard Gabriel Bazavan, Hongyi Xu, Bill Freeman, Rahul Sukthankar, Cristian Sminchisescu

Monocular 3D human pose and shape estimation is challenging due to the many degrees of freedom of the human body and thedifficulty to acquire training data for large-scale supervised learning in complex visual scenes.

Ranked #61 on 3D Human Pose Estimation on 3DPW (PA-MPJPE metric)

3D human pose and shape estimation Self-Supervised Learning

Human Synthesis and Scene Compositing

no code implementations23 Sep 2019 Mihai Zanfir, Elisabeta Oneata, Alin-Ionut Popa, Andrei Zanfir, Cristian Sminchisescu

Generating good quality and geometrically plausible synthetic images of humans with the ability to control appearance, pose and shape parameters, has become increasingly important for a variety of tasks ranging from photo editing, fashion virtual try-on, to special effects and image compression.

Image Compression Image Generation +1

Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images

no code implementations NeurIPS 2018 Andrei Zanfir, Elisabeta Marinoiu, Mihai Zanfir, Alin-Ionut Popa, Cristian Sminchisescu

The final stage of 3d pose and shape prediction is based on a learned attention process where information from different human body parts is optimally integrated.

Monocular 3D Pose and Shape Estimation of Multiple People in Natural Scenes - The Importance of Multiple Scene Constraints

no code implementations CVPR 2018 Andrei Zanfir, Elisabeta Marinoiu, Cristian Sminchisescu

Human sensing has greatly benefited from recent advances in deep learning, parametric human modeling, and large scale 2d and 3d datasets.

Human Appearance Transfer

no code implementations CVPR 2018 Mihai Zanfir, Alin-Ionut Popa, Andrei Zanfir, Cristian Sminchisescu

We propose an automatic person-to-person appearance transfer model based on explicit parametric 3d human representations and learned, constrained deep translation network architectures for photographic image synthesis.

Image Generation

Deep Learning of Graph Matching

no code implementations CVPR 2018 Andrei Zanfir, Cristian Sminchisescu

The problem of graph matching under node and pair-wise constraints is fundamental in areas as diverse as combinatorial optimization, machine learning or computer vision, where representing both the relations between nodes and their neighborhood structure is essential.

Combinatorial Optimization Graph Matching

Large Displacement 3D Scene Flow With Occlusion Reasoning

no code implementations ICCV 2015 Andrei Zanfir, Cristian Sminchisescu

In this paper we propose a novel coarse to fine correspondence-based scene flow approach to account for the effects of large displacements and to model occlusion, based on explicit geometric reasoning.

Motion Estimation Optical Flow Estimation

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