Search Results for author:
Found 16 papers, 6 papers with code
RenderIH: A Large-scale Synthetic Dataset for 3D Interacting Hand Pose Estimation
The current interacting hand (IH) datasets are relatively simplistic in terms of background and texture, with hand joints being annotated by a machine annotator, which may result in inaccuracies, and the diversity of pose distribution is limited.
Cloth2Tex: A Customized Cloth Texture Generation Pipeline for 3D Virtual Try-On
Since traditional warping-based texture generation methods require a significant number of control points to be manually selected for each type of garment, which can be a time-consuming and tedious process.
DiffHand: End-to-End Hand Mesh Reconstruction via Diffusion Models
Hand mesh reconstruction from the monocular image is a challenging task due to its depth ambiguity and severe occlusion, there remains a non-unique mapping between the monocular image and hand mesh.
Gloss-Free End-to-End Sign Language Translation
In this paper, we tackle the problem of sign language translation (SLT) without gloss annotations.
One-Shot High-Fidelity Talking-Head Synthesis with Deformable Neural Radiance Field
Talking head generation aims to generate faces that maintain the identity information of the source image and imitate the motion of the driving image.
Towards Stable Human Pose Estimation via Cross-View Fusion and Foot Stabilization
Then the optimization-based method is introduced to reconstruct the foot pose and foot-ground contact for the general multi-view datasets including AIST++ and Human3. 6M.
DART: Articulated Hand Model with Diverse Accessories and Rich Textures
Unity GUI is also provided to generate synthetic hand data with user-defined settings, e. g., pose, camera, background, lighting, textures, and accessories.
Jointly Harnessing Prior Structures and Temporal Consistency for Sign Language Video Generation
We optimize the two losses and keypoint detector network in an end-to-end manner.
One-stage Action Detection Transformer
In this work, we introduce our solution to the EPIC-KITCHENS-100 2022 Action Detection challenge.
Recurrent Dynamic Embedding for Video Object Segmentation
In this paper, we propose a Recurrent Dynamic Embedding (RDE) to build a memory bank of constant size.
Ranked #8 on
Semi-Supervised Video Object Segmentation
on MOSE
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+1
Multi-View Consistent Generative Adversarial Networks for 3D-aware Image Synthesis
To address this challenge, we propose Multi-View Consistent Generative Adversarial Networks (MVCGAN) for high-quality 3D-aware image synthesis with geometry constraints.
Divide and Rule: Recurrent Partitioned Network for Dynamic Processes
Differently, our goal is to represent a system with a part-whole hierarchy and discover the implied dependencies among intra-system variables: inferring the interactions that possess causal effects on the sub-system behavior with REcurrent partItioned Network (REIN).
VidFace: A Full-Transformer Solver for Video FaceHallucination with Unaligned Tiny Snapshots
In this paper, we investigate the task of hallucinating an authentic high-resolution (HR) human face from multiple low-resolution (LR) video snapshots.
OR-Net: Pointwise Relational Inference for Data Completion under Partial Observation
Specifically, we expect to approximate the real joint distribution over the partial observation and latent variables, thus infer the unseen targets respectively.
Learning Position and Target Consistency for Memory-based Video Object Segmentation
To address this limitation, we propose to Learn position and target Consistency framework for Memory-based video object segmentation, termed as LCM.
One-shot visual object segmentation
Semantic Segmentation
+1
Stable Learning in Coding Space for Multi-Class Decoding and Its Extension for Multi-Class Hypothesis Transfer Learning
Many prevalent multi-class classification approaches can be unified and generalized by the output coding framework which usually consists of three phases: (1) coding, (2) learning binary classifiers, and (3) decoding.
