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.
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.
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.
In this paper, we tackle the problem of sign language translation (SLT) without gloss annotations.
Talking head generation aims to generate faces that maintain the identity information of the source image and imitate the motion of the driving image.
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.
Unity GUI is also provided to generate synthetic hand data with user-defined settings, e. g., pose, camera, background, lighting, textures, and accessories.
We optimize the two losses and keypoint detector network in an end-to-end manner.
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
To address this challenge, we propose Multi-View Consistent Generative Adversarial Networks (MVCGAN) for high-quality 3D-aware image synthesis with geometry constraints.
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).
In this paper, we investigate the task of hallucinating an authentic high-resolution (HR) human face from multiple low-resolution (LR) video snapshots.
Specifically, we expect to approximate the real joint distribution over the partial observation and latent variables, thus infer the unseen targets respectively.
To address this limitation, we propose to Learn position and target Consistency framework for Memory-based video object segmentation, termed as LCM.
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.