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Found 29 papers, 19 papers with code
Vlogger: Make Your Dream A Vlog
More importantly, Vlogger can generate over 5-minute vlogs from open-world descriptions, without loss of video coherence on script and actor.
Latte: Latent Diffusion Transformer for Video Generation
We propose a novel Latent Diffusion Transformer, namely Latte, for video generation.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
The rapid development of open-source large language models (LLMs) has been truly remarkable.
Brush Your Text: Synthesize Any Scene Text on Images via Diffusion Model
To tackle this problem, we propose Diff-Text, which is a training-free scene text generation framework for any language.
EpiDiff: Enhancing Multi-View Synthesis via Localized Epipolar-Constrained Diffusion
Generating multiview images from a single view facilitates the rapid generation of a 3D mesh conditioned on a single image.
VBench: Comprehensive Benchmark Suite for Video Generative Models
We will open-source VBench, including all prompts, evaluation methods, generated videos, and human preference annotations, and also include more video generation models in VBench to drive forward the field of video generation.
SinSR: Diffusion-Based Image Super-Resolution in a Single Step
Extensive experiments conducted on synthetic and real-world datasets demonstrate that the proposed method can achieve comparable or even superior performance compared to both previous SOTA methods and the teacher model, in just one sampling step, resulting in a remarkable up to x10 speedup for inference.
SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction
The goal is to generate high-quality long videos with smooth and creative transitions between scenes and varying lengths of shot-level videos.
ConditionVideo: Training-Free Condition-Guided Text-to-Video Generation
In this work, we introduce ConditionVideo, a training-free approach to text-to-video generation based on the provided condition, video, and input text, by leveraging the power of off-the-shelf text-to-image generation methods (e. g., Stable Diffusion).
LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models
To this end, we propose LaVie, an integrated video generation framework that operates on cascaded video latent diffusion models, comprising a base T2V model, a temporal interpolation model, and a video super-resolution model.
Ranked #4 on
Text-to-Video Generation
on EvalCrafter Text-to-Video (ECTV) Dataset
(using extra training data)
LAC: Latent Action Composition for Skeleton-based Action Segmentation
In this context, we propose Latent Action Composition (LAC), a novel self-supervised framework aiming at learning from synthesized composable motions for skeleton-based action segmentation.
WeldMon: A Cost-effective Ultrasonic Welding Machine Condition Monitoring System
Ultrasonic welding machines play a critical role in the lithium battery industry, facilitating the bonding of batteries with conductors.
InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation
Specifically, we utilize a multi-scale approach to generate video-related descriptions.
AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning
Once trained, the motion module can be inserted into a personalized T2I model to form a personalized animation generator.
Self-Supervised Video Representation Learning via Latent Time Navigation
Self-supervised video representation learning aimed at maximizing similarity between different temporal segments of one video, in order to enforce feature persistence over time.
LEO: Generative Latent Image Animator for Human Video Synthesis
Our key idea is to represent motion as a sequence of flow maps in the generation process, which inherently isolate motion from appearance.
Long-Term Rhythmic Video Soundtracker
To this end, we present Long-Term Rhythmic Video Soundtracker (LORIS), a novel framework to synthesize long-term conditional waveforms.
Hierarchical Diffusion Autoencoders and Disentangled Image Manipulation
To mitigate those limitations, we propose Hierarchical Diffusion Autoencoders (HDAE) that exploit the fine-grained-to-abstract and lowlevel-to-high-level feature hierarchy for the latent space of diffusion models.
Learning Invariance from Generated Variance for Unsupervised Person Re-identification
This work focuses on unsupervised representation learning in person re-identification (ReID).
LAC - Latent Action Composition for Skeleton-based Action Segmentation
In this context, we propose Latent Action Composition (LAC), a novel self-supervised framework aiming at learning from synthesized composable motions for skeleton-based action segmentation.
3D-EPI Blip-Up/Down Acquisition (BUDA) with CAIPI and Joint Hankel Structured Low-Rank Reconstruction for Rapid Distortion-Free High-Resolution T2* Mapping
Purpose: This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitative T2* mapping.
ViA: View-invariant Skeleton Action Representation Learning via Motion Retargeting
Current self-supervised approaches for skeleton action representation learning often focus on constrained scenarios, where videos and skeleton data are recorded in laboratory settings.
Latent Image Animator: Learning to Animate Images via Latent Space Navigation
Specifically, motion in generated video is constructed by linear displacement of codes in the latent space.
Latent Image Animator: Learning to animate image via latent space navigation
Deviating from such models, we here introduce Latent Image Animator (LIA), a self-supervised auto-encoder that evades need for structure representation.
UNIK: A Unified Framework for Real-world Skeleton-based Action Recognition
This is achieved by learning an optimal dependency matrix from the uniform distribution based on a multi-head attention mechanism.
Ranked #1 on
Skeleton Based Action Recognition
on UPenn Action
InMoDeGAN: Interpretable Motion Decomposition Generative Adversarial Network for Video Generation
We design the architecture of InMoDeGAN-generator in accordance to proposed Linear Motion Decomposition, which carries the assumption that motion can be represented by a dictionary, with related vectors forming an orthogonal basis in the latent space.
Joint Generative and Contrastive Learning for Unsupervised Person Re-identification
In this context, we propose a mesh-based view generator.
Selective Spatio-Temporal Aggregation Based Pose Refinement System: Towards Understanding Human Activities in Real-World Videos
Taking advantage of human pose data for understanding human activities has attracted much attention these days.
G3AN: Disentangling Appearance and Motion for Video Generation
Creating realistic human videos entails the challenge of being able to simultaneously generate both appearance, as well as motion.
