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Found 8 papers, 2 papers with code
OccScene: Semantic Occupancy-based Cross-task Mutual Learning for 3D Scene Generation
Within OccScene, the perception module can be effectively improved with customized and diverse generated scenes, while the perception priors in return enhance the generation performance for mutual benefits.
Hierarchical Context Alignment with Disentangled Geometric and Temporal Modeling for Semantic Occupancy Prediction
Camera-based 3D Semantic Occupancy Prediction (SOP) is crucial for understanding complex 3D scenes from limited 2D image observations.
3D Semantic Occupancy Prediction
LIDAR Semantic Segmentation
+2
AVI-Talking: Learning Audio-Visual Instructions for Expressive 3D Talking Face Generation
In this paper, we propose AVI-Talking, an Audio-Visual Instruction system for expressive Talking face generation.
One at a Time: Progressive Multi-step Volumetric Probability Learning for Reliable 3D Scene Perception
Numerous studies have investigated the pivotal role of reliable 3D volume representation in scene perception tasks, such as multi-view stereo (MVS) and semantic scene completion (SSC).
Bridging Stereo Geometry and BEV Representation with Reliable Mutual Interaction for Semantic Scene Completion
However, due to the inherent representation gap between stereo geometry and BEV features, it is non-trivial to bridge them for dense prediction task of SSC.
Make Your Brief Stroke Real and Stereoscopic: 3D-Aware Simplified Sketch to Portrait Generation
Creating the photo-realistic version of people sketched portraits is useful to various entertainment purposes.
Masked Lip-Sync Prediction by Audio-Visual Contextual Exploitation in Transformers
This requires masking a large percentage of the original image and seamlessly inpainting it with the aid of audio and reference frames.
Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation
While speech content information can be defined by learning the intrinsic synchronization between audio-visual modalities, we identify that a pose code will be complementarily learned in a modulated convolution-based reconstruction framework.
