Recent progress has shown that video diffusion models (VDMs) can be repurposed to solve various multimodal graphics tasks. However, existing approaches predominantly train separate models for each specific problem setting. This practice locks models into fixed input-output mappings, and typically ignores the joint correlations across modalities. In this paper, we present UniVidX , a unified multimodal framework designed to leverage VDM priors to enable versatile video generation. Our goal is to (i) master diverse pixel-aligned tasks by formulating them as conditional generation problems within multimodal space, (ii) adapt to modality-specific distributions without compromising the backbone's native priors, and (iii) ensure cross-modal consistency during synthesis. Concretely, we propose three key designs: 1) Stochastic Condition Masking (SCM): by randomly partitioning modalities into clean conditions and noisy targets during training, we enable the model to learn omni-directional conditional generation rather than fixed mappings. 2) Decoupled Gated LoRA (DGL): we attach per-modality LoRAs and activate them when a modality serves as a generation target, thereby preserving the VDM's strong priors. 3) Cross-Modal Self-Attention (CMSA): we explicitly share keys/values across modalities while maintaining modality-specific queries, facilitating information exchange and inter-modal alignment. We validate our framework by instantiating it in two domains: 1) UniVid-Intrinsic for RGB videos and their intrinsic maps (albedo, irradiance, normal), and 2) UniVid-Alpha for blended RGB videos and their constituent RGBA layers. Experimental results demonstrate that both models achieve performance competitive with state-of-the-art methods across distinct tasks. Notably, they exhibit robust generalization capabilities in in-the-wild scenarios, even when trained on limited datasets of fewer than 1k videos.

Iteration-based methods have achieved impressive achievements in stereo matching tasks. However, due to the lack of disparity reliability judgment, existing iteration-based methods have difficulties obtaining reliable iterative results in a stable manner. In this paper, we propose a novel uncertainty-guided iterative architecture. Specifically, we first design a lightweight Cost volume-based disparity Uncertainty Estimation method (CUE), which efficiently accomplishes multiple inference during the iteration process. Subsequently, we propose Uncertainty-based Disparity update Control (UDC), which provides update magnitudes for different pixels that match the current disparity reliability. Additionally, we propose Uncertainty-based Disparity Rectification (UDR) for burden-free optimization of the initial disparity. Experiments on SceneFlow, KITTI, Middlebury 2014, and ETH3D demonstrate that the proposed method effectively improves the accuracy and efficiency of existing iterative methods. Particularly, when running only 15 updates, our method outperforms the baseline IGEV-Stereo that runs 32 updates, which saves around 40% of the inference time.

Neural rendering techniques, including NeRF and Gaussian Splatting (GS), rely on photometric consistency to produce high-quality reconstructions. However, in real-world driving scenarios, it is challenging to guarantee perfect photometric consistency in acquired images. Appearance codes have been widely used to address this issue, but their modeling capability is limited, as a single code is applied to the entire image. Recently, the bilateral grid was introduced to perform pixel-wise color mapping, but it is difficult to optimize and constrain effectively. In this paper, we propose a novel multi-scale bilateral grid that unifies appearance codes and bilateral grids. We demonstrate that this approach significantly improves geometric accuracy in dynamic, decoupled autonomous driving scene reconstruction, outperforming both appearance codes and bilateral grids. This is crucial for autonomous driving, where accurate geometry is important for obstacle avoidance and control. Our method shows strong results across four datasets: Waymo, NuScenes, Argoverse, and PandaSet. We further demonstrate that the improvement in geometry is driven by the multi-scale bilateral grid, which effectively reduces floaters caused by photometric inconsistency.

Due to the difficulty in obtaining real samples and ground truth, the generalization performance and domain adaptation performance are critical for the feasibility of stereo matching methods in practical applications. However, there are significant distributional discrepancies among different domains, which pose challenges for generalization and domain adaptation of the model. Inspired by the iteration-based methods, we propose a novel stepwise regression architecture. This architecture regresses the disparity error through multiple range-controlled clips, which effectively overcomes domain discrepancies. We implement this architecture based on the iterative-based methods, and refer to this new stereo method as SR-Stereo. Specifically, a new stepwise regression unit is proposed to replace the original update unit in order to control the range of output. Meanwhile, a regression objective segment is proposed to set the supervision individually for each stepwise regression unit. In addition, to enhance the edge awareness of models adapting new domains with sparse ground truth, we propose Domain Adaptation based on Pre-trained Edges (DAPE). In DAPE, a pre-trained stereo model and an edge estimator are used to estimate the edge maps of the target domain images, which along with the sparse ground truth disparity are used to fine-tune the stereo model. The proposed SR-Stereo and DAPE are extensively evaluated on SceneFlow, KITTI, Middbury 2014 and ETH3D. Compared with the SOTA methods and generalized methods, the proposed SR-Stereo achieves competitive in-domain and cross-domain performances. Meanwhile, the proposed DAPE significantly improves the performance of the fine-tuned model, especially in the texture-less and detailed regions. The code is available at https://github.com/zhuxing0/SR-Stereov1-DAPE