Image-to-Video Generation

Step-by-step painting tutorials are used for learning artistic techniques, but existing video resources lack interactivity and personalization. While recent generative models have advanced artistic image synthesis, they still struggle to generate temporally and structurally consistent videos. Video models excel in consistent frame generation, yet fine-tuning an image-to-video model on forward painting sequences creates a long-range dependency on the first-frame reference, disrupting its pretrained attention pattern and hindering reconstruction. We address this issue by training on reversed sequences that better preserve the reference-attention behavior. Building on this, we present Loomis Painter, a framework for reconstructing coherent painting processes. We further introduce a media-transfer training strategy that decouples input appearance from the target process, enabling generation across different artistic media. Trained on a curated dataset of real-world painting processes, our method outperforms existing baselines across LPIPS, DINO, and CLIP metrics and is consistently preferred in expert and non-expert user studies. Code available at https://github.com/Markus-Pobitzer/wlp.

Bridge Matching (BM) learns a stochastic transport between two data distributions and is therefore a natural tool for data-to-data tasks such as video frame interpolation, image-to-video generation, and video super-resolution. Existing bridge models for video, however, use a reference process that treats the frames of a clip independently, so temporal structure enters only through the network or the choice of the bridge endpoints. We propose Time-Correlated Video Bridge Matching (TCVBM), which places temporal structure into the reference process itself: the frames of a clip are coupled by a linear SDE whose drift is a discrete Laplacian along the time axis, and known frames enter as boundary conditions. The transition and bridge distributions remain Gaussian and available in closed form, so training and sampling are as simple as in standard BM, which is recovered in the limit of zero coupling. A spectral analysis shows that the prior damps high temporal frequencies, injects temporally smooth noise, and relaxes towards a boundary-determined profile, such as the linear interpolation between key frames. In controlled comparisons with frame-independent bridges under identical architectures and training budgets, TCVBM improves most automatic metrics for frame interpolation (Moving MNIST, UCF-101, Vimeo-90K) and image-to-video generation (Moving MNIST, UCF-101), e.g., reducing FVD from 688 to 611 for frame interpolation on UCF-101, and human raters prefer its frame interpolations for temporal consistency in 49$\%$ of comparisons versus 15$\%$ for the baseline ($p<10^{-17}$); for super-resolution, it performs on par with BM on automatic metrics.

Unified multimodal models have shown promising results in multimodal content generation and editing but remain largely limited to the image domain. In this work, we present UniVideo, a versatile framework that extends unified modeling to the video domain. UniVideo adopts a dual-stream design, combining a Multimodal Large Language Model (MLLM) for instruction understanding with a Multimodal DiT (MMDiT) for video generation. This design preserves the MLLM's original text generation capabilities, enables accurate interpretation of complex multimodal instructions, and maintains visual consistency in the generated content. Built on this architecture, UniVideo unifies diverse video generation and editing tasks under a single multimodal instruction paradigm and is jointly trained across them. Extensive experiments demonstrate that UniVideo matches or surpasses state-of-the-art task-specific baselines in text/image-to-video generation, in-context video generation and in-context video editing. Notably, the unified design of UniVideo enables two forms of generalization. First, UniVideo supports task composition, such as combining editing with style transfer, by integrating multiple capabilities within a single instruction. Second, even without explicit training on free-form video editing, UniVideo transfers its editing capability from large-scale image editing data to this setting, handling unseen instructions such as changing the environment or altering materials within a video. Beyond these core capabilities, UniVideo also supports visual-prompt-based video generation, where the MLLM interprets visual prompts and guides the MMDiT during synthesis. To foster future research, we released our model and code.

The rapid progress of image-guided video generation (I2V) has raised concerns about its potential misuse in misinformation and fraud, underscoring the urgent need for effective digital watermarking. While existing watermarking methods demonstrate robustness within a single modality, they fail to trace source images in I2V settings. To address this gap, we introduce the concept of Robust Diffusion Distance, which measures the temporal persistence of watermark signals in generated videos. Building on this, we propose I2VWM, a cross-modal watermarking framework designed to enhance watermark robustness across time. I2VWM leverages a video-simulation noise layer during training and employs an optical-flow-based alignment module during inference. Experiments on both open-source and commercial I2V models demonstrate that I2VWM significantly improves robustness while maintaining imperceptibility, establishing a new paradigm for cross-modal watermarking in the era of generative video. Code Released.

The synthesis of spatiotemporally coherent 4D content presents fundamental challenges in computer vision, requiring simultaneous modeling of high-fidelity spatial representations and physically plausible temporal dynamics. Current approaches often struggle to maintain view consistency while handling complex scene dynamics, particularly in large-scale environments with multiple interacting elements. This work introduces Dream4D, a novel framework that bridges this gap through a synergy of controllable video generation and neural 4D reconstruction. Our approach seamlessly combines a two-stage architecture: it first predicts optimal camera trajectories from a single image using few-shot learning, then generates geometrically consistent multi-view sequences via a specialized pose-conditioned diffusion process, which are finally converted into a persistent 4D representation. This framework is the first to leverage both rich temporal priors from video diffusion models and geometric awareness of the reconstruction models, which significantly facilitates 4D generation and shows higher quality (e.g., mPSNR, mSSIM) over existing methods.

Recent advances in 3D content generation have amplified demand for dynamic models that are both visually realistic and physically consistent. However, state-of-the-art video diffusion models frequently produce implausible results such as momentum violations and object interpenetrations. Existing physics-aware approaches often rely on task-specific fine-tuning or supervised data, which limits their scalability and applicability. To address the challenge, we present PhyMAGIC, a training-free framework that generates physically consistent motion from a single image. PhyMAGIC integrates a pre-trained image-to-video diffusion model, confidence-guided reasoning via LLMs, and a differentiable physics simulator to produce 3D assets ready for downstream physical simulation without fine-tuning or manual supervision. By iteratively refining motion prompts using LLM-derived confidence scores and leveraging simulation feedback, PhyMAGIC steers generation toward physically consistent dynamics. Comprehensive experiments demonstrate that PhyMAGIC outperforms state-of-the-art video generators and physics-aware baselines, enhancing physical property inference and motion-text alignment while maintaining visual fidelity.