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Found 16 papers, 11 papers with code
GS-DiT: Advancing Video Generation with Pseudo 4D Gaussian Fields through Efficient Dense 3D Point Tracking
Specifically, we propose a novel framework that constructs a pseudo 4D Gaussian field with dense 3D point tracking and renders the Gaussian field for all video frames.
BlinkVision: A Benchmark for Optical Flow, Scene Flow and Point Tracking Estimation using RGB Frames and Events
BlinkVision enables extensive benchmarks on three types of correspondence tasks (optical flow, point tracking, and scene flow estimation) for both image-based and event-based methods, offering new observations, practices, and insights for future research.
Stable Consistency Tuning: Understanding and Improving Consistency Models
Diffusion models achieve superior generation quality but suffer from slow generation speed due to the iterative nature of denoising.
Ranked #9 on
Image Generation
on ImageNet 64x64
Trans4D: Realistic Geometry-Aware Transition for Compositional Text-to-4D Synthesis
Then we propose a geometry-aware 4D transition network to realize a complex scene-level 4D transition based on the plan, which involves expressive geometrical object deformation.
Rectified Diffusion: Straightness Is Not Your Need in Rectified Flow
Building on this insight, we propose Rectified Diffusion, which generalizes the design space and application scope of rectification to encompass the broader category of diffusion models, rather than being restricted to flow-matching models.
OSV: One Step is Enough for High-Quality Image to Video Generation
Video diffusion models have shown great potential in generating high-quality videos, making them an increasingly popular focus.
Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT
Lumina-T2X is a nascent family of Flow-based Large Diffusion Transformers that establishes a unified framework for transforming noise into various modalities, such as images and videos, conditioned on text instructions.
Phased Consistency Models
In this paper, we identify three key flaws in the current design of Latent Consistency Models (LCMs).
Rethinking the Spatial Inconsistency in Classifier-Free Diffusion Guidance
Classifier-Free Guidance (CFG) has been widely used in text-to-image diffusion models, where the CFG scale is introduced to control the strength of text guidance on the whole image space.
Be-Your-Outpainter: Mastering Video Outpainting through Input-Specific Adaptation
We introduce MOTIA Mastering Video Outpainting Through Input-Specific Adaptation, a diffusion-based pipeline that leverages both the intrinsic data-specific patterns of the source video and the image/video generative prior for effective outpainting.
AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data
This paper introduces an effective method for computation-efficient personalized style video generation without requiring access to any personalized video data.
Motion-I2V: Consistent and Controllable Image-to-Video Generation with Explicit Motion Modeling
For the first stage, we propose a diffusion-based motion field predictor, which focuses on deducing the trajectories of the reference image's pixels.
Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising
To address this challenge, we introduce a novel paradigm dubbed as Gen-L-Video, capable of extending off-the-shelf short video diffusion models for generating and editing videos comprising hundreds of frames with diverse semantic segments without introducing additional training, all while preserving content consistency.
FOSTER: Feature Boosting and Compression for Class-Incremental Learning
The ability to learn new concepts continually is necessary in this ever-changing world.
Ranked #1 on
Incremental Learning
on ImageNet100 - 20 steps
Forward Compatible Few-Shot Class-Incremental Learning
Forward compatibility requires future new classes to be easily incorporated into the current model based on the current stage data, and we seek to realize it by reserving embedding space for future new classes.
Ranked #6 on
Few-Shot Class-Incremental Learning
on CIFAR-100
class-incremental learning
Few-Shot Class-Incremental Learning
+1
PyCIL: A Python Toolbox for Class-Incremental Learning
Traditional machine learning systems are deployed under the closed-world setting, which requires the entire training data before the offline training process.
