Trending Research
Less-to-More Generalization: Unlocking More Controllability by In-Context Generation
bytedance/uno
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In this study, we propose a highly-consistent data synthesis pipeline to tackle this challenge.
Conditional Image Generation
Personalized Image Generation
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REPA-E: Unlocking VAE for End-to-End Tuning with Latent Diffusion Transformers
We show that while diffusion loss is ineffective, end-to-end training can be unlocked through the representation-alignment (REPA) loss -- allowing both VAE and diffusion model to be jointly tuned during the training process.
Liquid: Language Models are Scalable Multi-modal Generators
We present Liquid, an auto-regressive generation paradigm that seamlessly integrates visual comprehension and generation by tokenizing images into discrete codes and learning these code embeddings alongside text tokens within a shared feature space for both vision and language.
Kimi-VL Technical Report
We present Kimi-VL, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers advanced multimodal reasoning, long-context understanding, and strong agent capabilities - all while activating only 2. 8B parameters in its language decoder (Kimi-VL-A3B).
Advanced Video Inpainting Using Optical Flow-Guided Efficient Diffusion
nevsnev/fgdvi
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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.
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.
The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search
AI is increasingly playing a pivotal role in transforming how scientific discoveries are made.
OctGPT: Octree-based Multiscale Autoregressive Models for 3D Shape Generation
octree-nn/octgpt
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In this paper, we introduce OctGPT, a novel multiscale autoregressive model for 3D shape generation that dramatically improves the efficiency and performance of prior 3D autoregressive approaches, while rivaling or surpassing state-of-the-art diffusion models.
VSLAM-LAB: A Comprehensive Framework for Visual SLAM Methods and Datasets
Visual Simultaneous Localization and Mapping (VSLAM) research faces significant challenges due to fragmented toolchains, complex system configurations, and inconsistent evaluation methodologies.
Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced Reasoning
xufangzhi/genius
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This motivates us to enhance LLM reasoning without the need for external supervision.
