Text-to-Video

3D asset generation is getting massive amounts of attention inspired by the recent success on text-guided 2D content creation. Existing text-to-3D methods use pretrained text-to-image diffusion models in an optimization problem or fine-tune them on synthetic data which often results in non-photorealistic 3D objects without backgrounds. In this paper we present a method that leverages pretrained text-to-image models as a prior and learn to generate multi-view images in a single denoising process from real-world data. Concretely we propose to integrate 3D volume-rendering and cross-frame-attention layers into each block of the existing U-Net network of the text-to-image model. Moreover we design an autoregressive generation that renders more 3D-consistent images at any viewpoint. We train our model on real-world datasets of objects and showcase its capabilities to generate instances with a variety of high-quality shapes and textures in authentic surroundings. Compared to the existing methods the results generated by our method are consistent and have favorable visual quality (-30% FID -37% KID).

Current diffusion-based video editing primarily focuses on structure-preserved editing by utilizing various dense correspondences to ensure temporal consistency and motion alignment. However these approaches are often ineffective when the target edit involves a shape change. To embark on video editing with shape change we explore customized video subject swapping in this work where we aim to replace the main subject in a source video with a target subject having a distinct identity and potentially different shape. In contrast to previous methods that rely on dense correspondences we introduce the VideoSwap framework that exploits semantic point correspondences inspired by our observation that only a small number of semantic points are necessary to align the subject's motion trajectory and modify its shape. We also introduce various user-point interactions (e.g. removing points and dragging points) to address various semantic point correspondence. Extensive experiments demonstrate state-of-the-art video subject swapping results across a variety of real-world videos.

Neural Radiance Fields (NeRFs) excel in photorealistically rendering static scenes. However rendering dynamic long-duration radiance fields on ubiquitous devices remains challenging due to data storage and computational constraints. In this paper we introduce VideoRF the first approach to enable real-time streaming and rendering of dynamic human-centric radiance fields on mobile platforms. At the core is a serialized 2D feature image stream representing the 4D radiance field all in one. We introduce a tailored training scheme directly applied to this 2D domain to impose the temporal and spatial redundancy of the feature image stream. By leveraging the redundancy we show that the feature image stream can be efficiently compressed by 2D video codecs which allows us to exploit video hardware accelerators to achieve real-time decoding. On the other hand based on the feature image stream we propose a novel rendering pipeline for VideoRF which has specialized space mappings to query radiance properties efficiently. Paired with a deferred shading model VideoRF has the capability of real-time rendering on mobile devices thanks to its efficiency. We have developed a real-time interactive player that enables online streaming and rendering of dynamic scenes offering a seamless and immersive free-viewpoint experience across a range of devices from desktops to mobile phones. Our project page is available at https://aoliao12138.github.io/VideoRF/.

Video grounding aims to localize a spatio-temporal section in a video corresponding to an input text query. This paper addresses a critical limitation in current video grounding methodologies by introducing an Open-Vocabulary Spatio-Temporal Video Grounding task. Unlike prevalent closed-set approaches that struggle with open-vocabulary scenarios due to limited training data and predefined vocabularies our model leverages pre-trained representations from foundational spatial grounding models. This empowers it to effectively bridge the semantic gap between natural language and diverse visual content achieving strong performance in closed-set and open-vocabulary settings. Our contributions include a novel spatio-temporal video grounding model surpassing state-of-the-art results in closed-set evaluations on multiple datasets and demonstrating superior performance in open-vocabulary scenarios. Notably the proposed model outperforms state-of-the-art methods in closed-set settings on VidSTG (Declarative and Interrogative) and HC-STVG (V1 and V2) datasets. Furthermore in open-vocabulary evaluations on HC-STVG V1 and YouCook-Interactions our model surpasses the recent best-performing models by 4.88 m_vIoU and 1.83 accuracy demonstrating its efficacy in handling diverse linguistic and visual concepts for improved video understanding. Our codes will be publicly released.

Text-to-video generation aims to produce a video based on a given prompt. Recently several commercial video models have been able to generate plausible videos with minimal noise excellent details and high aesthetic scores. However these models rely on large-scale well-filtered high-quality videos that are not accessible to the community. Many existing research works which train models using the low-quality WebVid-10M dataset struggle to generate high-quality videos because the models are optimized to fit WebVid-10M. In this work we explore the training scheme of video models extended from Stable Diffusion and investigate the feasibility of leveraging low-quality videos and synthesized high-quality images to obtain a high-quality video model. We first analyze the connection between the spatial and temporal modules of video models and the distribution shift to low-quality videos. We observe that full training of all modules results in a stronger coupling between spatial and temporal modules than only training temporal modules. Based on this stronger coupling we shift the distribution to higher quality without motion degradation by finetuning spatial modules with high-quality images resulting in a generic high-quality video model. Evaluations are conducted to demonstrate the superiority of the proposed method particularly in picture quality motion and concept composition.

Despite being (pre)trained on a massive amount of data state-of-the-art video-language alignment models are not robust to semantically-plausible contrastive changes in the video captions. Our work addresses this by identifying a broad spectrum of contrast misalignments such as replacing entities actions and flipping event order which alignment models should be robust against. To this end we introduce the VideoCon a video-language alignment dataset constructed by a large language model that generates plausible contrast video captions and explanations for differences between original and contrast video captions. Then a generative video-language model is finetuned with VideoCon to assess video-language entailment and generate explanations. Our VideoCon-based alignment model significantly outperforms current models. It exhibits a 12-point increase in AUC for the video-language alignment task on human-generated contrast captions. Finally our model sets new state of the art zero-shot performance in temporally-extensive video-language tasks such as text-to-video retrieval (SSv2-Temporal) and video question answering (ATP-Hard). Moreover our model shows superior performance on novel videos and human-crafted captions and explanations.

Text-driven video generation witnesses rapid progress. However merely using text prompts is not enough to depict the desired subject appearance that accurately aligns with users' intents especially for customized content creation. In this paper we study the task of video generation with image prompts which provide more accurate and direct content control beyond the text prompts. Specifically we propose a feed-forward framework VideoBooth with two dedicated designs: 1) We propose to embed image prompts in a coarse-to-fine manner. Coarse visual embeddings from image encoder provide high-level encodings of image prompts while fine visual embeddings from the proposed attention injection module provide multi-scale and detailed encoding of image prompts. These two complementary embeddings can faithfully capture the desired appearance. 2) In the attention injection module at fine level multi-scale image prompts are fed into different cross-frame attention layers as additional keys and values. This extra spatial information refines the details in the first frame and then it is propagated to the remaining frames which maintains temporal consistency. Extensive experiments demonstrate that VideoBooth achieves state-of-the-art performance in generating customized high-quality videos with subjects specified in image prompts. Notably VideoBooth is a generalizable framework where a single model works for a wide range of image prompts with only feed-forward passes.

Video-P2P is the first framework for real-world video editing with cross-attention control. While attention control has proven effective for image editing with pre-trained image generation models there are currently no large-scale video generation models publicly available. Video-P2P addresses this limitation by adapting an image generation diffusion model to complete various video editing tasks. Specifically we propose to first tune a Text-to-Set (T2S) model to complete an approximate inversion and then optimize a shared unconditional embedding to achieve accurate video inversion with a small memory cost. We further prove that it is crucial for consistent video editing. For attention control we introduce a novel decoupled-guidance strategy which uses different guidance strategies for the source and target prompts. The optimized unconditional embedding for the source prompt improves reconstruction ability while an initialized unconditional embedding for the target prompt enhances editability. Incorporating the attention maps of these two branches enables detailed editing. These technical designs enable various text-driven editing applications including word swap prompt refinement and attention re-weighting. Video-P2P works well on real-world videos for generating new characters while optimally preserving their original poses and scenes. It significantly outperforms previous approaches.

The creation of new datasets often presents new challenges for video recognition and can inspire novel ideas while addressing these challenges. While existing datasets mainly comprise landscape mode videos our paper seeks to introduce portrait mode videos to the research community and highlight the unique challenges associated with this video format. With the growing popularity of smartphones and social media applications recognizing portrait mode videos is becoming increasingly important. To this end we have developed the first dataset dedicated to portrait mode video recognition namely PortraitMode-400. The taxonomy of PortraitMode-400 was constructed in a data-driven manner comprising 400 fine-grained categories and rigorous quality assurance was implemented to ensure the accuracy of human annotations. In addition to the new dataset we conducted a comprehensive analysis of the impact of video format (portrait mode versus landscape mode) on recognition accuracy and spatial bias due to the different formats. Furthermore we designed extensive experiments to explore key aspects of portrait mode video recognition including the choice of data augmentation evaluation procedure the importance of temporal information and the role of audio modality. Building on the insights from our experimental results and the introduction of PortraitMode-400 our paper aims to inspire further research efforts in this emerging research area.

Most video captioning models are designed to process short video clips of few seconds and output text describing low-level visual concepts (e.g. objects scenes atomic actions). However most real-world videos last for minutes or hours and have a complex hierarchical structure spanning different temporal granularities. We propose Video ReCap a recursive video captioning model that can process video inputs of dramatically different lengths (from 1 second to 2 hours) and output video captions at multiple hierarchy levels. The recursive video-language architecture exploits the synergy between different video hierarchies and can process hour-long videos efficiently. We utilize a curriculum learning training scheme to learn the hierarchical structure of videos starting from clip-level captions describing atomic actions then focusing on segment-level descriptions and concluding with generating summaries for hour-long videos. Furthermore we introduce Ego4D-HCap dataset by augmenting Ego4D with 8267 manually collected long-range video summaries. Our recursive model can flexibly generate captions at different hierarchy levels while also being useful for other complex video understanding tasks such as VideoQA on EgoSchema. Data code and models are publicly available at https://sites.google.com/view/vidrecap.

Diffusion models have made significant strides in image generation mastering tasks such as unconditional image synthesis text-image translation and image-to-image conversions. However their capability falls short in the realm of video prediction mainly because they treat videos as a collection of independent images relying on external constraints such as temporal attention mechanisms to enforce temporal coherence. In our paper we introduce a novel model class that treats video as a continuous multi-dimensional process rather than a series of discrete frames. Through extensive experimentation we establish state-of-the-art performance in video prediction validated on benchmark datasets including KTH BAIR Human3.6M and UCF101.

We present VIDIM a generative model for video interpolation which creates short videos given a start and end frame. In order to achieve high fidelity and generate motions unseen in the input data VIDIM uses cascaded diffusion models to first generate the target video at low resolution and then generate the high-resolution video conditioned on the low-resolution generated video. We compare VIDIM to previous state-of-the-art methods on video interpolation and demonstrate how such works fail in most settings where the underlying motion is complex nonlinear or ambiguous while VIDIM can easily handle such cases. We additionally demonstrate how classifier-free guidance on the start and end frame and conditioning the superresolution model on the original high-resolution frames without additional parameters unlocks high-fidelity results. VIDIM is fast to sample from as it jointly denoises all the frames to be generated requires less than a billion parameters per diffusion model to produce compelling results and still enjoys scalability and improved quality at larger parameter counts. Please see our project page at vidiminterpolation.github.io.

Video harmonization is an important and challenging task that aims to obtain visually realistic composite videos by automatically adjusting the foreground's appearance to harmonize with the background. Inspired by the short-term and long-term gradual adjustment process of manual harmonization we present a Video Triplet Transformer framework to model three spatio-temporal variation patterns within videos i.e. short-term spatial as well as long-term global and dynamic for video-to-video tasks like video harmonization. Specifically for short-term harmonization we adjust foreground appearance to consist with background in spatial dimension based on the neighbor frames; for long-term harmonization we not only explore global appearance variations to enhance temporal consistency but also alleviate motion offset constraints to align similar contextual appearances dynamically. Extensive experiments and ablation studies demonstrate the effectiveness of our method achieving state-of-the-art performance in video harmonization video enhancement and video demoireing tasks. We also propose a temporal consistency metric to better evaluate the harmonized videos. Code is available at https://github.com/zhenglab/VideoTripletTransformer.

Video Frame Interpolation (VFI) has witnessed a surge in popularity due to its abundant downstream applications. Event-based VFI (E-VFI) has recently propelled the advancement of VFI. Thanks to the high temporal resolution benefits event cameras can bridge the informational void present between successive video frames. Most state-of-the-art E-VFI methodologies follow the conventional VFI paradigm which pivots on motion estimation between consecutive frames to generate intermediate frames through a process of warping and refinement. However this reliance engenders a heavy dependency on the quality and consistency of keyframes rendering these methods susceptible to challenges in extreme real-world scenarios such as missing moving objects and severe occlusion dilemmas. This study proposes a novel E-VFI framework that directly synthesize intermediate frames leveraging event-based reference obviating the necessity for explicit motion estimation and substantially enhancing the capacity to handle motion occlusion. Given the sparse and inherently noisy nature of event data we prioritize the reliability of the event-based reference leading to the development of an innovative event-aware reconstruction strategy for accurate reference generation. Besides we implement a bi-directional event-guided alignment from keyframes to the reference using the introduced E-PCD module. Finally a transformer-based decoder is adopted for prediction refinement. Comprehensive experimental evaluations on both synthetic and real-world datasets underscore the superiority of our approach and its potential to execute high-quality VFI tasks.

In this paper we propose VidLA an approach for video-language alignment at scale. There are two major limitations of previous video-language alignment approaches. First they do not capture both short-range and long-range temporal dependencies and typically employ complex hierarchical deep network architectures that are hard to integrate with existing pretrained image-text foundation models. To effectively address this limitation we instead keep the network architecture simple and use a set of data tokens that operate at different temporal resolutions in a hierarchical manner accounting for the temporally hierarchical nature of videos. By employing a simple two-tower architecture we are able to initialize our video-language model with pretrained image-text foundation models thereby boosting the final performance. Second existing video-language alignment works struggle due to the lack of semantically aligned large-scale training data. To overcome it we leverage recent LLMs to curate the largest video-language dataset to date with better visual grounding. Furthermore unlike existing video-text datasets which only contain short clips our dataset is enriched with video clips of varying durations to aid our temporally hierarchical data tokens in extracting better representations at varying temporal scales. Overall empirical results show that our proposed approach surpasses state-of-the-art methods on multiple retrieval benchmarks especially on longer videos and performs competitively on classification benchmarks.

Vision-Language models (VLMs) have excelled in the image-domain--- especially in zero-shot settings--- thanks to the availability of vast pretraining data (i.e. paired image-text samples). However for videos such paired data is not as abundant. Therefore video-VLMs are usually designed by adapting pretrained image-VLMs to the video-domain instead of training from scratch. All such recipes rely on augmenting visual embeddings with temporal information (i.e. image --> video) often keeping text embeddings unchanged or even being discarded. In this paper we argue the contrary that better video-VLMs can be designed by focusing more on augmenting text rather than visual information. More specifically we introduce Video-conditioned Text Representations (VicTR): a form of text embeddings optimized w.r.t. visual embeddings creating a more-flexible contrastive latent space. Our model can further make use of freely-available semantic information in the form of visually-grounded auxiliary text (e.g. object or scene information). We evaluate our model on few-shot zero-shot (HMDB-51 UCF-101) short-form (Kinetics-400) and long-form (Charades) activity recognition benchmarks showing strong performance among video-VLMs.

Large language models (LLMs) have shown remarkable text understanding capabilities which have been extended as Video LLMs to handle video data for comprehending visual details. However existing Video LLMs can only provide a coarse description of the entire video failing to capture the precise start and end time boundary of specific events. In this paper we solve this issue via proposing VTimeLLM a novel Video LLM designed for fine-grained video moment understanding and reasoning with respect to time boundary. Specifically our VTimeLLM adopts a boundary-aware three-stage training strategy which respectively utilizes image-text pairs for feature alignment multiple-event videos to increase temporal-boundary awareness and high-quality video-instruction tuning to further improve temporal understanding ability as well as align with human intents. Extensive experiments demonstrate that in fine-grained time-related comprehension tasks for videos such as Temporal Video Grounding and Dense Video Captioning VTimeLLM significantly outperforms existing Video LLMs. Besides benefits from the fine-grained temporal understanding of the videos further enable VTimeLLM to beat existing Video LLMs in video dialogue benchmark showing its superior cross-modal understanding and reasoning abilities.

Text-to-video diffusion models have advanced video generation significantly. However customizing these models to generate videos with tailored motions presents a substantial challenge. In specific they encounter hurdles in (1) accurately reproducing motion from a target video and (2) creating diverse visual variations. For example straightforward extensions of static image customization methods to video often lead to intricate entanglements of appearance and motion data. To tackle this here we present the Video Motion Customization (VMC) framework a novel one-shot tuning approach crafted to adapt temporal attention layers within video diffusion models. Our approach introduces a novel motion distillation objective using residual vectors between consecutive noisy latent frames as a motion reference. The diffusion process then preserve low-frequency motion trajectories while mitigating high-frequency motion-unrelated noise in image space. We validate our method against state-of-the-art video generative models across diverse real-world motions and contexts. Our codes and data can be found at: https://video-motion-customization.github.io/

Recent advances in large video-language models have displayed promising outcomes in video comprehension. Current approaches straightforwardly convert video into language tokens and employ large language models for multi-modal tasks. However this method often leads to the generation of irrelevant content commonly known as "hallucination" as the length of the text increases and the impact of the video diminishes. To address this problem we propose Vista-LLaMA a novel framework that maintains the consistent distance between all visual tokens and any language tokens irrespective of the generated text length. Vista-LLaMA omits relative position encoding when determining attention weights between visual and text tokens retaining the position encoding for text and text tokens. This amplifies the effect of visual tokens on text generation especially when the relative distance is longer between visual and text tokens. The proposed attention mechanism significantly reduces the chance of producing irrelevant text related to the video content. Furthermore we present a sequential visual projector that projects the current video frame into tokens of language space with the assistance of the previous frame. This approach not only captures the temporal relationship within the video but also allows less visual tokens to encompass the entire video. Our approach significantly outperforms various previous methods (e.g. Video-ChatGPT MovieChat) on four challenging open-ended video question answering benchmarks. We reach an accuracy of 60.7 on the zero-shot NExT-QA and 60.5 on the zero-shot MSRVTT-QA setting a new state-of-the-art performance.