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- Interpreting the Latent Space of GANs for Semantic Face Editing1,275
This work proposes a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs, and finds that the latent code of well-trained generative models actually learns a disentangled representation after linear transformations.
- In-Domain GAN Inversion for Real Image Editing697
An in-domain GAN inversion approach, which not only faithfully reconstructs the input image but also ensures the inverted code to be semantically meaningful for editing, which achieves satisfying real image reconstruction and facilitates various image editing tasks, significantly outperforming start-of-the-arts.
- Closed-Form Factorization of Latent Semantics in GANs664
This work examines the internal representation learned by GANs to reveal the underlying variation factors in an unsupervised manner and proposes a closedform factorization algorithm for latent semantic discovery by directly decomposing the pre-trained weights.
- AnyDoor: Zero-shot Object-level Image Customization539
This work presents AnyDoor, a diffusion-based image generator with the power to teleport target objects to new scenes at user-specified locations with desired shapes, and proposes to borrow knowledge from video datasets, which can observe various forms of a single object, leading to stronger model generalizability and robustness.
- Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels536
It is argued that every pixel matters to the model training, even its prediction is ambiguous, and an effective pipeline is developed to make sufficient use of unlabeled data to separate reliable and unreliable pixels via the entropy of predictions.
- Notice of Removal: VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation442
This work presents a decomposed diffusion process via resolving the per-frame noise into a base noise that is shared among all frames and a residual noise that varies along the time axis, which surpasses both GAN-based and diffusion-based alternatives in high-quality video generation.
- Composer: Creative and Controllable Image Synthesis with Composable Conditions404
This work offers a new generation paradigm that allows flexible control of the output image, such as spatial layout and palette, while maintaining the synthesis quality and model creativity, and serves as a general framework and facilitates a wide range of classical generative tasks without retraining.
- Image Processing Using Multi-Code GAN Prior347
A novel approach is proposed, called mGANprior, to incorporate the well-trained GANs as effective prior to a variety of image processing tasks, by employing multiple latent codes to generate multiple feature maps at some intermediate layer of the generator and composing them with adaptive channel importance to recover the input image.
- SpatialTracker: Tracking Any 2D Pixels in 3D Space220
This work proposes to estimate point trajectories in 3D space to mitigate the issues caused by image projection, and achieves state-of-the-art tracking performance both qualitatively and quantitatively, particularly in challenging scenarios such as out-of-plane rotation.
- Semantic Hierarchy Emerges in Deep Generative Representations for Scene Synthesis216
This work shows that highly-structured semantic hierarchy emerges in the deep generative representations from the state-of-the-art GANs like StyleGAN and BigGAN, trained for scene synthesis, and quantifies the causality between the layer-wise activations and the semantics occurring in the output image.
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- 3D-aware Image Synthesis via Learning Structural and Textural Representations145
This work proposes a novel framework, termed as VolumeGAN, for high-fidelity 3D-aware image synthesis, through explicitly learning a structural representation and a textural representation in a Generative Adversarial Network.
- Advancing Open-source World Models142
LingBot-World is presented, an open-sourced world simulator stemming from video generation that maintains high fidelity and robust dynamics in a broad spectrum of environments, including realism, scientific contexts, cartoon styles, and beyond.
- 4K4D: Real-Time 4D View Synthesis at 4K Resolution136
4K4D, a 4D point cloud representation that supports hardware rasterization and network pre-computation to enable unprecedented rendering speed with a high rendering quality, is proposed and a novel hybrid appearance model is designed that significantly boosts the rendering quality while preserving efficiency.
- Generative Hierarchical Features from Synthesizing Images126
This work considers the pre-trained StyleGAN generator as a learned loss function and utilizes its layer-wise representation to train a novel hierarchical encoder, termed as Generative Hierarchical Feature (GH-Feat), which has strong transferability to both generative and discriminative tasks.
- Learning Temporally Consistent Video Depth from Video Diffusion Priors118
This work reformulates depth prediction into a conditional generation problem to provide contextual information within a clip and across clips to provide cross-clip context and proposes a consistent context-aware training and inference strategy for arbitrarily long videos to provide cross-clip context.
- Cones 2: Customizable Image Synthesis with Multiple Subjects114
This work finds that the text embedding regarding the subject token already serves as a simple yet effective representation that supports arbitrary combinations without any model tuning and proposes to employ layout, a very abstract and easy-to-obtain prior, as the spatial guidance for subject arrangement.
- SpatialTrackerV2: 3D Point Tracking Made Easy101
SpatialTrackerV2 decomposes world-space 3D motion into scene geometry, camera ego-motion, and pixel-wise object motion, with a fully differentiable and end-to-end architecture, allowing scalable training across a wide range of datasets.
- Ranni: Taming Text-to-Image Diffusion for Accurate Instruction Following95
This work introduces a semantic panel as the middleware in decoding texts to images, supporting the generator to better follow instructions, and enhances a pre-trained T2I generator regarding its textual controllability.
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- DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-aware Scene Synthesis83
The proposed model spatially disentangles the whole scene into object-centric generative radiance fields by learning on only 2D images with the global-local discrimination, and obtains the generation fidelity and editing flexibility of individual objects while being able to efficiently compose objects and the background into a complete scene.
- Scaling Instruction-Based Video Editing with a High-Quality Synthetic Dataset82
Ditto is introduced, a holistic framework that fuses the creative diversity of a leading image editor with an in-context video generator, overcoming the limited scope of existing models and resolves the prohibitive cost-quality trade-off.
- InFusion: Inpainting 3D Gaussians via Learning Depth Completion from Diffusion Prior75
This work proposes to guide the point initialization with an image-conditioned depth completion model, which learns to directly restore the depth map based on the observed image, and surpasses existing alternatives with sufficiently better fidelity and efficiency under various complex scenarios.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. 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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