Trending Research
NdLinear Is All You Need for Representation Learning
We propose NdLinear as a drop-in replacement for standard linear layers -- marking an important step toward next-generation neural architectures.
IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech System
Recently, large language model (LLM) based text-to-speech (TTS) systems have gradually become the mainstream in the industry due to their high naturalness and powerful zero-shot voice cloning capabilities. Here, we introduce the IndexTTS system, which is mainly based on the XTTS and Tortoise model.
Audio-visual Controlled Video Diffusion with Masked Selective State Spaces Modeling for Natural Talking Head Generation
To this end, we introduce \textbf{ACTalker}, an end-to-end video diffusion framework that supports both multi-signals control and single-signal control for talking head video generation.
Pushing the Limits of Large Language Model Quantization via the Linearity Theorem
hanguo97/flute
•
•
Quantizing large language models has become a standard way to reduce their memory and computational costs.
Advanced Video Inpainting Using Optical Flow-Guided Efficient Diffusion
nevsnev/fgdvi
•
•
Specifically, FloED employs a dual-branch architecture, where a flow branch first restores corrupted flow and a multi-scale flow adapter provides motion guidance to the main inpainting branch.
LocAgent: Graph-Guided LLM Agents for Code Localization
By parsing codebases into directed heterogeneous graphs, LocAgent creates a lightweight representation that captures code structures (files, classes, functions) and their dependencies (imports, invocations, inheritance), enabling LLM agents to effectively search and locate relevant entities through powerful multi-hop reasoning.
LHM: Large Animatable Human Reconstruction Model from a Single Image in Seconds
aigc3d/LHM
•
•
Animatable 3D human reconstruction from a single image is a challenging problem due to the ambiguity in decoupling geometry, appearance, and deformation.
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
Efficiently acquiring external knowledge and up-to-date information is essential for effective reasoning and text generation in large language models (LLMs).
ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency Policy
cccedric/conrft
•
•
This work highlights the potential of integrating reinforcement learning to enhance the performance of VLA models for real-world robotic applications.
Olympus: A Universal Task Router for Computer Vision Tasks
We introduce Olympus, a new approach that transforms Multimodal Large Language Models (MLLMs) into a unified framework capable of handling a wide array of computer vision tasks.
