TC-Light: Revolutionizing Long Video Relighting with Temporal Consistency and Efficiency

Introduction: The Critical Challenge of Video Relighting

In the rapidly evolving landscape of digital content creation and embodied AI, video relighting has emerged as a transformative technology. This technique enables creators to manipulate illumination in video sequences while preserving intrinsic image details – a capability with profound implications for:

• Visual Content Production: Allowing filmmakers to adjust lighting conditions without reshoots
• Augmented Reality: Creating seamless integration between virtual and real-world lighting
• Embodied AI Training: Generating diverse, photorealistic training data through sim2real transfer

However, existing solutions face two fundamental limitations when processing long videos with complex dynamics:

1. Temporal Inconsistency: Noticeable flickering between frames
2. Computational Overhead: Prohibitive resource requirements for real-time applications

This article introduces TC-Light, a breakthrough framework that addresses these challenges through innovative temporal optimization techniques. We’ll explore how this method achieves state-of-the-art results while maintaining practical efficiency.

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The Evolution of Video Relighting Technology

From Static Images to Dynamic Scenes

Early relighting approaches focused primarily on static images, leveraging techniques like:

• Light-stage data training: Physical capture systems for illumination modeling
• Diffusion-based generators: Recent advances like LightIt and SwitchLight

While these methods excel in controlled environments, they struggle with highly dynamic videos where:

• Foreground objects frequently enter/exit the frame
• Camera motion creates complex parallax effects
• Lighting conditions vary significantly across frames

Current State-of-the-Art Limitations

Recent video relighting approaches can be categorized into three groups:

Approach Type	Examples	Limitations
Portrait-specific	[57, 9, 6]	Restricted to human subjects
High-compute models	[60, 16]	OOM errors on long sequences
Zero-shot adaptations	VidToMe, Slicedit	Trade-offs between consistency/quality

Our benchmark testing (Table 2) reveals critical shortcomings in existing methods:

• 逐帧处理 (per-frame processing): Causes severe illumination flicker (Fig. 3a)
• Complex 3D representations: NeRF/3DGS models require 10-30 minutes per video
• Domain limitations: Cosmos-Transfer1 fails on highly dynamic scenes

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TC-Light: A Two-Stage Optimization Framework

Core Innovation

TC-Light introduces a novel paradigm characterized by decoupled temporal optimization. The system architecture consists of:

1. Base Relighting Model: Zero-shot adaptation of IC-Light using VidToMe’s token merging
2. Two-Stage Post-Optimization:

   • Stage I: Global illumination alignment
   • Stage II: Fine-grained texture refinement

Key Technical Components

1. Decayed Multi-Axis Denoising

To balance motion guidance with illumination control:

ε_θ^V(·,p) = √γ_τ * ε_θ^xy(·,p) + √(1-γ_τ) * ε_θ^yt(·,"")

Where:

• γ_τ decays exponentially during denoising
• Adaptive Instance Normalization (AIN) aligns feature statistics
• Preserves source motion while reducing texture bias

2. Stage I: Exposure Alignment

Per-frame affine transformation matrix optimization:

L_exposure = (1-λ_e)L_photo(Ĩ_t,I_t) + λ_eL_1(Ĩ_t ⊙ M_t, Warp_{t+1→t}(Ĩ_{t+1}) ⊙ M_t)

Soft mask calculation using flow and RGB error metrics:

M_t = sigmoid(β(ξ_flow - E_flow)) ⊙ sigmoid(β(ξ_rgb - E_rgb))

3. Stage II: Unique Video Tensor Optimization

Compact 1D representation compression:

U(κ_n) = Avg({I_t^in(x,y) | κ(x,y,t)=κ_n})

Optimization objective combining multiple constraints:

L_unique = λ_tvL_tv(Ĩ_t) + (1-λ_u)L_SSIM(Ĩ_t,Ĩ_t) + λ_uL_1(Ĩ_t ⊙ M_t, Warp_{t+1→t}(Ĩ_{t+1}) ⊙ M_t)

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Experimental Validation

Benchmark Construction

We established a comprehensive evaluation framework containing:

Dataset	Type	Resolution	Avg Frames	Modalities
SceneFlow	Synthetic	960×512	960	C,F,D,S
CARLA	Synthetic	960×536	208	C,D,S
Waymo	Real	960×640	198	C
DRONE	Real	1280×720	213	C

Full dataset details in Table 1

Evaluation Metrics

1. Temporal Consistency:

   • Motion Smoothness (Motion-S)
   • Warping SSIM (Warp-SSIM)

2. Textual Alignment:

   • CLIP embedding similarity (CLIP-T)

3. User Preference:

   • Bradley-Terry preference rate (User-PF)

4. Computation:

   • FPS and VRAM usage

Quantitative Results

Method	Motion-S↑	Warp-SSIM↑	CLIP-T↑	User-PF↑	FPS↑	VRAM(G)↓
IC-Light*	94.52%	71.22	0.2743	10.97%	0.123	16.49
VidToMe	95.38%	73.69	0.2731	6.97%	0.409	11.65
TC-Light	97.80%	91.75	0.2679	23.96%	0.204	14.37

Key advantages highlighted in red

Qualitative Analysis

Key observations from visual results:

• Eliminates flickering artifacts present in per-frame methods
• Maintains object identity better than Slicedit
• Avoids unnatural lighting patterns seen in Cosmos-Transfer1

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Ablation Studies and Insights

Component Contribution Analysis

Configuration	Motion-S↑	Warp-SSIM↑	CLIP-T↑	VRAM(G)
Baseline	94.51%	77.60	0.2871	10.63
+ Stage I	95.71%	81.29	0.2868	11.33
+ Stage II (UVT)	96.44%	91.04	0.2866	11.81
+ Decayed Multi-Axis	97.75%	93.74	0.2865	11.57

Unique Video Tensor (UVT) Analysis

Scene	Compression Rate	SSIM	PSNR	LPIPS
CARLA	39.2%	0.994	50.71	0.025
InteriorNet	49.0%	0.991	46.17	0.021

UVT demonstrates near-lossless compression capabilities

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Limitations and Future Directions

Current constraints include:

1. Base model limitations in handling hard shadows
2. Resolution constraints (minimum 512px)
3. Potential over-smoothing in textureless regions
4. Dependency on optical flow estimation quality

Future improvements could focus on:

• Enhanced base illumination models
• Alternative canonical representations
• More efficient temporal consistency mechanisms

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Conclusion

TC-Light represents a significant advancement in video relighting technology through:

• Novel two-stage optimization framework
• Unique Video Tensor representation
• Efficient computation characteristics
• Superior temporal consistency

This breakthrough enables practical applications in:

• Content creation workflows
• Embodied AI training pipelines
• Real-time augmented reality systems

As video content continues to dominate digital media, solutions like TC-Light will play crucial roles in expanding creative possibilities while maintaining computational feasibility.