We present InstantMesh, a feed-forward framework for instant 3D mesh generation from a single image, featuring state-of-the-art generation quality and significant training scalability.
This module converts the generated sequence of images into videos with smooth transitions and consistent subjects that are significantly more stable than the modules based on latent spaces only, especially in the context of long video generation.
In this work, we propose a novel Trajectory Score Matching (TSM) method that aims to solve the pseudo ground truth inconsistency problem caused by the accumulated error in Interval Score Matching (ISM) when using the Denoising Diffusion Implicit Models (DDIM) inversion process.
The paper introduces AniTalker, an innovative framework designed to generate lifelike talking faces from a single portrait.
We present the workflow of Online Iterative Reinforcement Learning from Human Feedback (RLHF) in this technical report, which is widely reported to outperform its offline counterpart by a large margin in the recent large language model (LLM) literature.
We explore the intersection of LLMs and penetration testing to gain insight into their capabilities and challenges in the context of privilege escalation.
With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications.
MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation.
MolCA enables an LM (e. g., Galactica) to understand both text- and graph-based molecular contents via the cross-modal projector.
Ranked #4 on Molecule Captioning on ChEBI-20
Based on this pipeline, a random face reference training method is further devised to precisely capture the ID-relevant embeddings from reference images, thus improving the fidelity and generalization capacity of our model for ID-specific video generation.