Trending Research
Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models
We present Text2Room, a method for generating room-scale textured 3D meshes from a given text prompt as input.
Zero-1-to-3: Zero-shot One Image to 3D Object
We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image.
LLaMA: Open and Efficient Foundation Language Models
tatsu-lab/stanford_alpaca
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arXiv 2023
We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters.
Ranked #1 on
Question Answering
on PIQA
VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking
Our core insight is to predict objects directly based on sparse voxel features, without relying on hand-crafted proxies.
Ranked #1 on
3D Object Detection
on Argoverse2
A Dynamic Multi-Scale Voxel Flow Network for Video Prediction
megvii-research/CVPR2023-DMVFN
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The performance of video prediction has been greatly boosted by advanced deep neural networks.
Ranked #1 on
Video Prediction
on DAVIS 2017
VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation
A diffusion probabilistic model (DPM), which constructs a forward diffusion process by gradually adding noise to data points and learns the reverse denoising process to generate new samples, has been shown to handle complex data distribution.
Ranked #4 on
Video Generation
on UCF-101
Learning Context-aware Classifier for Semantic Segmentation
Semantic segmentation is still a challenging task for parsing diverse contexts in different scenes, thus the fixed classifier might not be able to well address varying feature distributions during testing.
NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping
To bridge this gap, in this paper, we propose a novel NeRF-based LiDAR odometry and mapping approach, NeRF-LOAM, consisting of three modules neural odometry, neural mapping, and mesh reconstruction.
Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models
To this end, We build a system called \textbf{Visual ChatGPT}, incorporating different Visual Foundation Models, to enable the user to interact with ChatGPT by 1) sending and receiving not only languages but also images 2) providing complex visual questions or visual editing instructions that require the collaboration of multiple AI models with multi-steps.
CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognition
Attempting to train the visual and text encoder to account for this shift results in catastrophic forgetting and a notable decrease in performance.
