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Found 15 papers, 12 papers with code
Training-Free Efficient Video Generation via Dynamic Token Carving
Despite the remarkable generation quality of video Diffusion Transformer (DiT) models, their practical deployment is severely hindered by extensive computational requirements.
Magic Mirror: ID-Preserved Video Generation in Video Diffusion Transformers
We present Magic Mirror, a framework for generating identity-preserved videos with cinematic-level quality and dynamic motion.
DreamOmni: Unified Image Generation and Editing
We begin by analyzing existing frameworks and the requirements of downstream tasks, proposing a unified framework that integrates both T2I models and various editing tasks.
Lyra: An Efficient and Speech-Centric Framework for Omni-Cognition
As Multi-modal Large Language Models (MLLMs) evolve, expanding beyond single-domain capabilities is essential to meet the demands for more versatile and efficient AI.
Ranked #1 on
Visual Question Answering (VQA)
on EgoSchema
ControlNeXt: Powerful and Efficient Control for Image and Video Generation
In this paper, we propose ControlNeXt: a powerful and efficient method for controllable image and video generation.
ResMaster: Mastering High-Resolution Image Generation via Structural and Fine-Grained Guidance
Diffusion models excel at producing high-quality images; however, scaling to higher resolutions, such as 4K, often results in over-smoothed content, structural distortions, and repetitive patterns.
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
We try to narrow the gap by mining the potential of VLMs for better performance and any-to-any workflow from three aspects, i. e., high-resolution visual tokens, high-quality data, and VLM-guided generation.
Prompt Highlighter: Interactive Control for Multi-Modal LLMs
Without tuning on LLaVA-v1. 5, our method secured 70. 7 in the MMBench test and 1552. 5 in MME-perception.
Make-Your-Video: Customized Video Generation Using Textual and Structural Guidance
Our method, dubbed Make-Your-Video, involves joint-conditional video generation using a Latent Diffusion Model that is pre-trained for still image synthesis and then promoted for video generation with the introduction of temporal modules.
Real-World Image Variation by Aligning Diffusion Inversion Chain
Our pipeline enhances the generation quality of image variations by aligning the image generation process to the source image's inversion chain.
Video-P2P: Video Editing with Cross-attention Control
This paper presents Video-P2P, a novel framework for real-world video editing with cross-attention control.
CodeTalker: Speech-Driven 3D Facial Animation with Discrete Motion Prior
In this paper, we propose to cast speech-driven facial animation as a code query task in a finite proxy space of the learned codebook, which effectively promotes the vividness of the generated motions by reducing the cross-modal mapping uncertainty.
Ranked #4 on
3D Face Animation
on BEAT2
Ref-NPR: Reference-Based Non-Photorealistic Radiance Fields for Controllable Scene Stylization
We propose a ray registration process based on the stylized reference view to obtain pseudo-ray supervision in novel views.
PCL: Proxy-Based Contrastive Learning for Domain Generalization
Domain generalization refers to the problem of training a model from a collection of different source domains that can directly generalize to the unseen target domains.
Ranked #23 on
Domain Generalization
on PACS
High Quality Segmentation for Ultra High-resolution Images
To segment 4K or 6K ultra high-resolution images needs extra computation consideration in image segmentation.
