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Found 4 papers, 2 papers with code
InfNeRF: Towards Infinite Scale NeRF Rendering with O(log n) Space Complexity
The conventional mesh-based Level of Detail (LoD) technique, exemplified by applications such as Google Earth and many game engines, exhibits the capability to holistically represent a large scene even the Earth, and achieves rendering with a space complexity of O(log n).
HiFiHR: Enhancing 3D Hand Reconstruction from a Single Image via High-Fidelity Texture
We present HiFiHR, a high-fidelity hand reconstruction approach that utilizes render-and-compare in the learning-based framework from a single image, capable of generating visually plausible and accurate 3D hand meshes while recovering realistic textures.
Taming Diffusion Models for Music-driven Conducting Motion Generation
Generating the motion of orchestral conductors from a given piece of symphony music is a challenging task since it requires a model to learn semantic music features and capture the underlying distribution of real conducting motion.
Understanding Capacity-Driven Scale-Out Neural Recommendation Inference
One approach to support this scale is with distributed serving, or distributed inference, which divides the memory requirements of a single large model across multiple servers.
