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Found 9 papers, 1 papers with code
PSHuman: Photorealistic Single-view Human Reconstruction using Cross-Scale Diffusion
Detailed and photorealistic 3D human modeling is essential for various applications and has seen tremendous progress.
MMTrail: A Multimodal Trailer Video Dataset with Language and Music Descriptions
To fulfill this gap, we present MMTrail, a large-scale multi-modality video-language dataset incorporating more than 20M trailer clips with visual captions, and 2M high-quality clips with multimodal captions.
Multi-View Large Reconstruction Model via Geometry-Aware Positional Encoding and Attention
Despite recent advancements in the Large Reconstruction Model (LRM) demonstrating impressive results, when extending its input from single image to multiple images, it exhibits inefficiencies, subpar geometric and texture quality, as well as slower convergence speed than expected.
CoCoGesture: Toward Coherent Co-speech 3D Gesture Generation in the Wild
Here, we construct the audio ControlNet through a trainable copy of our pre-trained diffusion model.
Era3D: High-Resolution Multiview Diffusion using Efficient Row-wise Attention
Specifically, these methods assume that the input images should comply with a predefined camera type, e. g. a perspective camera with a fixed focal length, leading to distorted shapes when the assumption fails.
Intensive Vision-guided Network for Radiology Report Generation
Given the above limitation in feature extraction, we propose a Globally-intensive Attention (GIA) module in the medical image encoder to simulate and integrate multi-view vision perception.
RustNeRF: Robust Neural Radiance Field with Low-Quality Images
First, existing methods assume enough high-quality images are available for training the NeRF model, ignoring real-world image degradation.
Weakly-Supervised Emotion Transition Learning for Diverse 3D Co-speech Gesture Generation
In addition, the lack of large-scale available datasets with emotional transition speech and corresponding 3D human gestures also limits the addressing of this task.
Learning from Inside: Self-driven Siamese Sampling and Reasoning for Video Question Answering
To consider the interdependent knowledge between contextual clips into the network inference, we propose a Siamese Sampling and Reasoning (SiaSamRea) approach, which consists of a siamese sampling mechanism to generate sparse and similar clips (i. e., siamese clips) from the same video, and a novel reasoning strategy for integrating the interdependent knowledge between contextual clips into the network.
