Trending Research
DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation
dreamgaussian/dreamgaussian
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In contrast to the occupancy pruning used in Neural Radiance Fields, we demonstrate that the progressive densification of 3D Gaussians converges significantly faster for 3D generative tasks.
ProPainter: Improving Propagation and Transformer for Video Inpainting
sczhou/propainter
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ICCV 2023
We also propose a mask-guided sparse video Transformer, which achieves high efficiency by discarding unnecessary and redundant tokens.
Ranked #1 on
Video Inpainting
on DAVIS
Demystifying CLIP Data
We believe that the main ingredient to the success of CLIP is its data and not the model architecture or pre-training objective.
Text-to-3D using Gaussian Splatting
gsgen3d/gsgen
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In this stage, we increase the number of Gaussians by compactness-based densification to enhance continuity and improve fidelity.
Deep Geometrized Cartoon Line Inbetweening
lisiyao21/animeinbet
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ICCV 2023
To preserve the precision and detail of the line drawings, we propose a new approach, AnimeInbet, which geometrizes raster line drawings into graphs of endpoints and reframes the inbetweening task as a graph fusion problem with vertex repositioning.
Qwen Technical Report
QwenLM/Qwen-7B
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Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
3D Gaussian Splatting for Real-Time Radiance Field Rendering
graphdeco-inria/gaussian-splatting
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Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos.
InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition
internlm/internlm-xcomposer
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We propose InternLM-XComposer, a vision-language large model that enables advanced image-text comprehension and composition.
Communicative Agents for Software Development
At the core of this paradigm lies ChatDev, a virtual chat-powered software development company that mirrors the established waterfall model, meticulously dividing the development process into four distinct chronological stages: designing, coding, testing, and documenting.
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
LongLoRA adopts LLaMA2 7B from 4k context to 100k, or LLaMA2 70B to 32k on a single 8x A100 machine.
