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Found 10 papers, 8 papers with code
VLOGGER: Multimodal Diffusion for Embodied Avatar Synthesis
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.
Score Distillation Sampling with Learned Manifold Corrective
Score Distillation Sampling (SDS) is a recent but already widely popular method that relies on an image diffusion model to control optimization problems using text prompts.
TAPE: Temporal Attention-based Probabilistic human pose and shape Estimation
Existing methods ignore the ambiguities of the reconstruction and provide a single deterministic estimate for the 3D pose.
Ranked #50 on
3D Human Pose Estimation
on 3DPW
Probabilistic Modeling for Human Mesh Recovery
This paper focuses on the problem of 3D human reconstruction from 2D evidence.
Ranked #62 on
3D Human Pose Estimation
on Human3.6M
(PA-MPJPE metric)
Birds of a Feather: Capturing Avian Shape Models from Images
We learn models of multiple species from the CUB dataset, and contribute new species-specific and multi-species shape models that are useful for downstream reconstruction tasks.
3D Bird Reconstruction: a Dataset, Model, and Shape Recovery from a Single View
Automated capture of animal pose is transforming how we study neuroscience and social behavior.
Coherent Reconstruction of Multiple Humans from a Single Image
Our goal is to train a single network that learns to avoid these problems and generate a coherent 3D reconstruction of all the humans in the scene.
Ranked #2 on
3D Human Reconstruction
on AGORA
TexturePose: Supervising Human Mesh Estimation with Texture Consistency
Assuming that the texture of the person does not change dramatically between frames, we can apply a novel texture consistency loss, which enforces that each point in the texture map has the same texture value across all frames.
Ranked #27 on
Weakly-supervised 3D Human Pose Estimation
on Human3.6M
Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop
Our approach is self-improving by nature, since better network estimates can lead the optimization to better solutions, while more accurate optimization fits provide better supervision for the network.
Convolutional Mesh Regression for Single-Image Human Shape Reconstruction
Image-based features are attached to the mesh vertices and the Graph-CNN is responsible to process them on the mesh structure, while the regression target for each vertex is its 3D location.
Ranked #34 on
Monocular 3D Human Pose Estimation
on Human3.6M
3D Hand Pose Estimation
3D human pose and shape estimation
+2
