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Found 11 papers, 6 papers with code
FineControlNet: Fine-level Text Control for Image Generation with Spatially Aligned Text Control Injection
While ControlNet provides control over the geometric form of the instances in the generated image, it lacks the capability to dictate the visual appearance of each instance.
HandNeRF: Learning to Reconstruct Hand-Object Interaction Scene from a Single RGB Image
The inference as well as training-data generation for 3D hand-object scene reconstruction is challenging due to the depth ambiguity of a single image and occlusions by the hand and object.
Three Recipes for Better 3D Pseudo-GTs of 3D Human Mesh Estimation in the Wild
Recovering 3D human mesh in the wild is greatly challenging as in-the-wild (ITW) datasets provide only 2D pose ground truths (GTs).
Ranked #6 on
3D Multi-Person Pose Estimation
on MuPoTS-3D
Rethinking Self-Supervised Visual Representation Learning in Pre-training for 3D Human Pose and Shape Estimation
Recently, a few self-supervised representation learning (SSL) methods have outperformed the ImageNet classification pre-training for vision tasks such as object detection.
MonoNHR: Monocular Neural Human Renderer
Existing neural human rendering methods struggle with a single image input due to the lack of information in invisible areas and the depth ambiguity of pixels in visible areas.
HandOccNet: Occlusion-Robust 3D Hand Mesh Estimation Network
However, we argue that occluded regions have strong correlations with hands so that they can provide highly beneficial information for complete 3D hand mesh estimation.
Ranked #5 on
3D Hand Pose Estimation
on DexYCB
Learning to Estimate Robust 3D Human Mesh from In-the-Wild Crowded Scenes
Second, we propose a joint-based regressor that distinguishes a target person's feature from others.
Ranked #10 on
3D Multi-Person Pose Estimation
on MuPoTS-3D
2D Human Pose Estimation
3D Multi-Person Human Pose Estimation
+1
NeuralAnnot: Neural Annotator for 3D Human Mesh Training Sets
Assuming no 3D pseudo-GTs are available, NeuralAnnot is weakly supervised with GT 2D/3D joint coordinates of training sets.
Accurate 3D Hand Pose Estimation for Whole-Body 3D Human Mesh Estimation
Using Pose2Pose, Hand4Whole utilizes hand MCP joint features to predict 3D wrists as MCP joints largely contribute to 3D wrist rotations in the human kinematic chain.
Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video
Our TCMR significantly outperforms previous video-based methods in temporal consistency with better per-frame 3D pose and shape accuracy.
Ranked #64 on
3D Human Pose Estimation
on MPI-INF-3DHP
Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose
Most of the recent deep learning-based 3D human pose and mesh estimation methods regress the pose and shape parameters of human mesh models, such as SMPL and MANO, from an input image.
Ranked #19 on
3D Hand Pose Estimation
on FreiHAND
