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.
We believe that the main ingredient to the success of CLIP is its data and not the model architecture or pre-training objective.
In this stage, we increase the number of Gaussians by compactness-based densification to enhance continuity and improve fidelity.
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
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.
Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos.
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.
Recently years have witnessed a rapid development of large language models (LLMs).
LongLoRA adopts LLaMA2 7B from 4k context to 100k, or LLaMA2 70B to 32k on a single 8x A100 machine.
While recently Multimodal Large Language Models (MM-LLMs) have made exciting strides, they mostly fall prey to the limitation of only input-side multimodal understanding, without the ability to produce content in multiple modalities.