Diversity-Aware Sign Language Production through a Pose Encoding Variational Autoencoder

This paper addresses the problem of diversity-aware sign language production, where we want to give an image (or sequence) of a signer and produce another image with the same pose but different attributes (\textit{e.g.} gender, skin color). To this end, we extend the variational inference paradigm to include information about the pose and the conditioning of the attributes. This formulation improves the quality of the synthesised images. The generator framework is presented as a UNet architecture to ensure spatial preservation of the input pose, and we include the visual features from the variational inference to maintain control over appearance and style. We generate each body part with a separate decoder. This architecture allows the generator to deliver better overall results. Experiments on the SMILE II dataset show that the proposed model performs quantitatively better than state-of-the-art baselines regarding diversity, per-pixel image quality, and pose estimation. Quantitatively, it faithfully reproduces non-manual features for signers.

Novel-View Human Action Synthesis

Novel-View Human Action Synthesis aims to synthesize the appearance of a dynamic scene from a virtual viewpoint, given a video from a real viewpoint. Our approach uses a novel 3D reasoning to synthesize the target viewpoint. We first estimate the 3D mesh of the target object, a human actor, and transfer the rough textures from the 2D images to the mesh. This transfer may generate sparse textures on the mesh due to frame resolution or occlusions. To solve this problem, we produce a semi-dense textured mesh by propagating the transferred textures both locally, within local geodesic neighborhoods, and globally, across symmetric semantic parts. Next, we introduce a context-based generator to learn how to correct and complete the residual appearance information. This allows the network to independently focus of learning the foreground and background synthesis tasks. We validate the proposed solution on the public NTU RGB+D dataset. The code and resources are available at \url{https://mlakhal.github.io/novel-view_action_synthesis/}.