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- Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data2,160
This work extends the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data and employs a U-Net discriminator with spectral normalization to increase discriminator capability and stabilize the training dynamics.
- Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation1,235
This work proposes a new T2V generation setting—One-Shot Video Tuning, where only one text-video pair is presented, and introduces Tune-A-Video, which involves a tailored spatio-temporal attention mechanism and an efficient one-shot tuning strategy.
- YOLO-World: Real-Time Open-Vocabulary Object Detection1,134
YOLO-World is introduced, an innovative approach that enhances YOLO with open-vocabulary detection capabilities through vision-language modeling and pre-training on large-scale datasets and proposes a new Re-parameterizable Vision-Language Path Aggregation Network (RepVL-PAN) and region-text contrastive loss to facilitate the interaction between visual and linguistic information.
- SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension1,018
This work addresses the evaluation of generative comprehension in MLLMs as a preliminary step towards a comprehensive assessment ofGenerative models, by introducing a benchmark named SEED-Bench, which spans 12 evaluation dimensions including the comprehension of both the image and video modality.
- MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing902
MasaCtrl converts existing self-attention in diffusion models into mutual self-attention, so that it can query correlated local contents and textures from source images for consistency, and proposes a mask-guided mutual self-attention strategy.
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- VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models710
This work explores the training scheme of video models extended from Stable Diffusion and investigates the feasibility of leveraging low-quality videos and synthesized high-quality images to obtain a high-quality video model, resulting in a generic high-quality video model.
- Towards Real-World Blind Face Restoration with Generative Facial Prior676
This work proposes GFP-GAN that leverages rich and diverse priors encapsulated in a pretrained face GAN for blind face restoration that achieves superior performance to prior art on both synthetic and real-world datasets.
- MotionCtrl: A Unified and Flexible Motion Controller for Video Generation673
MotionCtrl is presented, a unified and flexible motion controller for video generation designed to effectively and independently control camera and object motion and is a relatively generalizable model that can adapt to a wide array of camera poses and trajectories once trained.
- VideoCrafter1: Open Diffusion Models for High-Quality Video Generation633
This model is the first open-source I2V foundation model capable of transforming a given image into a video clip while maintaining content preservation constraints, and can generate realistic and cinematic-quality videos with a resolution of $1024 \times 576.
- FateZero: Fusing Attentions for Zero-shot Text-based Video Editing594
F FateZero, a zero-shot text-based editing method on real-world videos without per-prompt training or use-specific mask, is proposed, which is the first one to show the ability of zero-shot text-driven video style and local attribute editing from the trained text-to-image model.
- InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models569
InstantMesh is presented, a feed-forward framework for instant 3D mesh generation from a single image, featuring state-of-the-art generation quality and significant training scalability, and is able to create diverse 3D assets within 10 seconds.
- SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation542
SadTalker is presented, which generates 3D motion coefficients (head pose, expression) of the 3DMM from audio and implicitly modulates a novel 3D-aware face render for talking head generation and designs PoseVAE via a conditional VAE to synthesize head motion in different styles.
- Deep Crossing460
The Deep Crossing model is proposed which is a deep neural network that automatically combines features to produce superior models and was able to build, from scratch, two web-scale models for a major paid search engine, and achieve superior results with only a sub-set of the features used in the production models.
- EvalCrafter: Benchmarking and Evaluating Large Video Generation Models416
This work argues that it is hard to judge the large conditional generative models from the simple metrics since these models are often trained on very large datasets with multi-aspect abilities, and proposes a novel framework and pipeline for exhaustively evaluating the performance of the generated videos.
- PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding410
PhotoMaker is introduced, an efficient personalized text-to-image generation method, which mainly encodes an arbitrary number of input ID images into a stack ID embed-ding for preserving ID information, and an ID-oriented data construction pipeline to assemble the training data.
- Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free Videos382
A novel two-stage training scheme that can utilize easily obtained datasets and the pre-trained text-to-image (T2I) model to obtain the pose-controllable character videos and successfully generates continuously pose-controllable character videos while keeps the editing and concept composition ability of the pre-trained T2I model.
- LayoutDiffusion: Controllable Diffusion Model for Layout-to-Image Generation369
A diffusion model named LayoutDiffusion is proposed that can obtain higher generation quality and greater controllability than the previous works and is proposed to construct a structural image patch with region information and transform the patched image into a special layout to fuse with the normal layout in a unified form.
- SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation365
This work presents a unified and versatile foundation model, namely, SEED-X, which is able to model multi-granularity visual semantics for comprehension and generation tasks and demonstrates its effectiveness in handling real-world applications across various domains after instruction tuning.
- UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition365
Large kernels are the key to unlocking the exceptional performance of ConvNets in domains where they were originally not proficient, and the proposed model achieves state- of-the-art performance on time-series forecasting and audio recognition tasks even without modality-specific customization to the architecture.
- Instances as Queries362
For the first time, it is demonstrated that a simple end-to-end query based framework can achieve the state-of-the-art performance in various instance-level recognition tasks.
- GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction340
The GPT4Tools based on self-instruct to enable open-source LLMs, such as LLaMA and OPT, to use tools, generates an instruction-following dataset by prompting an advanced teacher with various multi-modal contexts using the Low-Rank Adaptation (LoRA) optimization.
- Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models334
This paper proposes a new framework called Mix-of-Show, which adopts an embedding-decomposed LoRA (ED-LoRA) for single-client tuning and gradient fusion for the center node to preserve the in-domain essence of single concepts and support theoretically limitless concept fusion.
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- GS-IR: 3D Gaussian Splatting for Inverse Rendering300
The GS-IR is a novel inverse rendering approach based on 3D Gaussian Splatting that leverages forward mapping volume rendering to achieve photorealistic novel view synthesis and relighting results and demonstrates the superiority of the method over baseline methods through qualitative and quantitative evaluations of various challenging scenes.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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