We propose Uni-Animator, a novel Diffusion Transformer (DiT)-based framework for unified image and video sketch colorization. Existing sketch colorization methods struggle to unify image and video tasks, suffering from imprecise color transfer with single or multiple references, inadequate preservation of high-frequency physical details, and compromised temporal coherence with motion artifacts in large-motion scenes. To tackle imprecise color transfer, we introduce visual reference enhancement via instance patch embedding, enabling precise alignment and fusion of reference color information. To resolve insufficient physical detail preservation, we design physical detail reinforcement using physical features that effectively capture and retain high-frequency textures. To mitigate motion-induced temporal inconsistency, we propose sketch-based dynamic RoPE encoding that adaptively models motion-aware spatial-temporal dependencies. Extensive experimental results demonstrate that Uni-Animator achieves competitive performance on both image and video sketch colorization, matching that of task-specific methods while unlocking unified cross-domain capabilities with high detail fidelity and robust temporal consistency.
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Figure 2: Workflow Comparison: Manual Colorization vs. Ours . Our automated framework eliminates repetitive frame-by-frame adjustments and cross-domain adaptation efforts, significantly reducing labor costs in industrial production.
Figure 3: Motivation: Limitations of Existing Methods vs. Our Solution . Existing methods suffer from style deviation (left), detail degradation (right), and temporal flickering (bottom) when handling images or videos separately. Our unified framework addresses all three issues concurrently.
Figure 4: Overall architecture of Uni-Animator. Given input visual references, the VAE encoder and physical projector encode its physical features and concatenate with visual latent features, then the DiT model predicts subsequent frames conditioned on physical, visual, and text embeddings. Sketch-based Dynamic RoPE to improve temporal inconsistency in large motion dynamic scenes without extra training.
Figure 5: Visual Control Ability . Our method supports accurate semantic matching for image colorization with over five visual references. For video colorization, it enables dynamic customization of characters by altering visual references while preserving original motions.
Figure 6: Visualization of RoPE frequency comparison. Strong horizontal motion (a) triggers higher frequency oscillations compared to gentle vertical motion (b), matching the asymmetric motion patterns observed in sketch sequences.
Figure 7: Comparison of Visual Results with Baselines . Our method shows better visual consistency during image and video sketch colorization, supporting single to multiple visual references dynamically. Compared with other video methods that use the first frame as input, our method shows a competitive result and the ability to customize the coloring results without the first frame.
Figure 8: Ablation Result of each component. After removing the Visual Reference Enhancement and PhysEncoder, the consistency of detail semantics decreases. When dynamic encoding is installed, the movement of the main subject becomes more natural.