Search Results for author:
Found 7 papers, 4 papers with code
MotionChain: Conversational Motion Controllers via Multimodal Prompts
However, this proficiency remains largely unexplored in other multimodal generative models, particularly in human motion models.
M3DBench: Let's Instruct Large Models with Multi-modal 3D Prompts
Furthermore, we establish a new benchmark for assessing the performance of large models in understanding multi-modal 3D prompts.
ShapeGPT: 3D Shape Generation with A Unified Multi-modal Language Model
The advent of large language models, enabling flexibility through instruction-driven approaches, has revolutionized many traditional generative tasks, but large models for 3D data, particularly in comprehensively handling 3D shapes with other modalities, are still under-explored.
VQ-NeRF: Vector Quantization Enhances Implicit Neural Representations
Recent advancements in implicit neural representations have contributed to high-fidelity surface reconstruction and photorealistic novel view synthesis.
A Large-Scale Outdoor Multi-modal Dataset and Benchmark for Novel View Synthesis and Implicit Scene Reconstruction
Meanwhile, a new benchmark for several outdoor NeRF-based tasks is established, such as novel view synthesis, surface reconstruction, and multi-modal NeRF.
Coordinates Are NOT Lonely -- Codebook Prior Helps Implicit Neural 3D Representations
Implicit neural 3D representation has achieved impressive results in surface or scene reconstruction and novel view synthesis, which typically uses the coordinate-based multi-layer perceptrons (MLPs) to learn a continuous scene representation.
Accurate Estimation of Body Height From a Single Depth Image via a Four-Stage Developing Network
Non-contact measurement of human body height can be very difficult under some circumstances. In this paper we address the problem of accurately estimating the height of a person with arbitrary postures from a single depth image.
