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Works38 from public data
- 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.
- Latent Video Diffusion Models for High-Fidelity Long Video Generation260
This work introduces lightweight video diffusion models that synthesize high-fidelity and arbitrary-long videos from pure noise that outperforms previous methods on 3D pixel space when under a limited computational budget and introduces conditional latent perturbation to reduce performance degradation during generating long-duration videos.
- FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling205
This work proposes FreeNoise, a tuning-free and time-efficient paradigm to enhance the generative capabilities of pretrained video diffusion models while preserving content consistency, and designs a novel motion injection method to support the generation of videos conditioned on multiple text prompts.
- Follow-Your-Emoji: Fine-Controllable and Expressive Freestyle Portrait Animation199
Follow-Your-Emoji, a diffusion-based framework for portrait animation, which animates a reference portrait with target landmark sequences, is presented and a facial fine-grained loss is proposed to improve the model’s ability of subtle expression perception and reference portrait appearance reconstruction.
- Make-Your-Video: Customized Video Generation Using Textual and Structural Guidance166
This paper explores customized video generation by utilizing text as context description and motion structure (e.g., frame-wise depth) as concrete guidance as concrete guidance in joint-conditional video generation using a Latent Diffusion Model.
- Seeing and Hearing: Open-domain Visual-Audio Generation with Diffusion Latent Aligners152
This work proposes a mul-timodality latent aligner with the pre-trained ImageBind model that shares a similar core as the clas-sifier guidance that guides the diffusion denoising process during inference time, and shows the superior performance of the method on joint video-audio generation, visual-steered audio generation, and audio-steered vi-sual generation tasks.
- ScaleCrafter: Tuning-free Higher-Resolution Visual Generation with Diffusion Models150
This work investigates the capability of generating images from pre-trained diffusion models at much higher resolutions than the training image sizes and proposes the dispersed convolution and noise-damped classifier-free guidance, which can enable ultra-high-resolution image generation.
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- VideoDPO: Omni-Preference Alignment for Video Diffusion Generation131
This work designs a pipeline to automatically collect preference pair data based on the proposed OmniScore and discovers that re-weighting these pairs based on the score significantly impacts overall preference alignment.
- Animate-A-Story: Storytelling with Retrieval-Augmented Video Generation106
This work develops a framework comprised of two functional modules, which provides video candidates with desired scene or motion context described by query texts, and generates plot-aligned videos under the guidance of motion structure and text prompts.
- Interactive Story Visualization with Multiple Characters84
A system for generic interactive story visualization, capable of handling multiple novel characters and supporting the editing of layout and local structure is proposed, developed by leveraging the prior knowledge of large language and T2I models, trained on massive corpora.
- Follow-Your-Click: Open-domain Regional Image Animation via Motion Prompts73
This paper proposes a practical framework, named Follow-Your-Click, to achieve image animation with a simple user click and a motion prompt, and proposes flow-based motion magnitude control to control the speed of target movement more precisely.
- Follow-Your-Click: Open-domain Regional Image Animation via Short Prompts68
This paper proposes a practical framework, named Follow-Your-Click, to achieve image animation with a simple user click and a short motion prompt and has simpler yet precise user control and better generation performance than previous methods.
- MagicStick: Controllable Video Editing via Control Handle Transformations66
This method is the first method to show the ability of video property editing from the pre-trained text-to-image model, and compares with shape-aware text-based editing and handcrafted motion video generation, demonstrating the superior temporal consistency and editing capability than previous works.
- FreeTraj: Tuning-Free Trajectory Control in Video Diffusion Models61
A tuning-free framework to achieve trajectory-controllable video generation, by imposing guidance on both noise construction and attention computation, and proposes FreeTraj, a tuning-free approach that enables trajectory control by modifying noise sampling and attention mechanisms.
- Follow-Your-Emoji-Faster: Towards Efficient, Fine-Controllable, and Expressive Freestyle Portrait Animation49
Stable Diffusion is enhanced with two key components: a expression-aware landmarks as explicit motion signals, which improve motion alignment, support exaggerated expressions, and reduce identity leakage; and a fine-grained facial loss that leverages both expression and facial masks to better capture subtle expressions and faithfully preserve the reference appearance.
- LLMs Meet Multimodal Generation and Editing: A Survey42
This work provides a systematic and insightful overview of multimodal generation and processing, which is expected to advance the development of Artificial Intelligence for Generative Content (AIGC) and world models.
- Unsupervised Portrait Shadow Removal via Generative Priors34
This work proposes the first unsupervised method for portrait shadow removal without any training data, and leverages the generative facial priors embedded in the off-the-shelf pretrained StyleGAN2 to leverage the decomposition process.
- Large Motion Video Autoencoding with Cross-modal Video VAE21
A novel and powerful video autoencoder capable of high-fidelity video encoding with temporal-aware spatial compression to better encode and decode the spatial information and a lightweight motion compression model for further temporal compression.
- MMTrail: A Multimodal Trailer Video Dataset with Language and Music Descriptions18
This work presents MMTrail, a large-scale multi-modality video-language dataset incorporating more than 20M trailer clips with visual captions, and 2M high-quality clips with multimodal captions incorporating various modality annotations, and proposes a systemic captioning framework.
- Deep Learning Models for Automatic Upper Airway Segmentation and Minimum Cross-Sectional Area Localisation in Two-Dimensional Images18
A fully automatic AI-driven system for upper airway segmentation and CSAmin localisation using 2D radiographic airway images is successfully developed and validated using convolutional neural network algorithms.
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- ModelGrow: Continual Text-to-Video Pre-training with Model Expansion and Language Understanding Enhancement4
This work explores the continual general pre-training of text-to-video models, enabling the model to "grow" its abilities based on a pre-trained foundation, analogous to how humans acquire new knowledge based on past experiences.
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- SpA2V: Harnessing Spatial Auditory Cues for Audio-driven Spatially-aware Video Generation3
SpA2V is presented, the first framework explicitly exploits spatial auditory cues from audios to generate videos with high semantic and spatial correspondence and develops an efficient and effective approach to seamlessly integrate VSLs as conditional guidance into pre-trained diffusion models, enabling VSL-grounded video generation in a training-free manner.
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- AnimationBench: Are Video Models Good at Character-Centric Animation?1
AnimationBench is introduced, the first systematic benchmark for evaluating animation image-to-video generation that operationalizes the Twelve Basic Principles of Animation and IP Preservation into measurable evaluation dimensions, together with Broader Quality Dimensions including semantic consistency, motion rationality, and camera motion consistency.
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- Interpreting Class Conditional GANs with Channel Awareness1
Channel awareness is proposed, which quantitatively characterizes how a single channel contributes to the final synthesis of multiple classes in the class conditional generator, and shows promising potential in segmenting the synthesized image and evaluating the category-wise synthesis performance.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. 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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