DiPIR: Infers real-world lighting conditions from a single image, making it possible to insert virtual objects into images or videos so that they appear to be real in the scene.
The project aims to solve the problem of inserting virtual objects into images or videos so that they look like they really exist in the scene. Traditional methods usually fail to achieve complete realism when dealing with lighting, shadows and reflections.
The study proposes a new technology, called DiPIR , that can insert arbitrary virtual objects into pictures or videos and make them blend into the original images or videos, making them look like they really exist in the scene.
This method is particularly suitable for indoor and outdoor images or videos, and can automatically adjust the material and lighting of objects so that they blend naturally with the scene. Experimental results show that this method performs well in multiple test scenes and generates very realistic images.
DiPIR Solves the Following Key Problems:
Challenges of lighting estimation:
Estimating scene lighting in a single image is an ill-posed problem, especially in images captured by consumer devices with low dynamic range. Traditional methods often perform poorly in dealing with such complex scenes, resulting in inserted virtual objects that do not match the real scene.
Realism of lighting and shadow effects:
Virtual object insertion requires accurate lighting and shadow effects, including shadows, reflections, etc., to ensure that the virtual object looks like part of the scene. Although existing diffusion models are powerful in image generation, they are still insufficient in handling such complex lighting and shadow details.
Personalization:
Generic diffusion models often do not adapt well to specific scenarios. DiPIR makes lightweight and personalized adjustments to the diffusion model to adapt it to specific scenarios, thereby improving the realism of the insertion effect.
DiPIR makes lightweight and personalized adjustments to the diffusion model to adapt it to specific scenarios
Features of DiPIR
1. Physically Based Inverse Rendering
Physically accurate lighting simulation: DiPIR uses a physically based renderer to simulate the interaction between light and 3D objects in the scene, accurately reproducing lighting effects such as shadows, reflections, and highlights. This accurate lighting simulation allows virtual objects to blend seamlessly with the real environment after being inserted into the scene.
2. Diffusion model guidance
Visual priors trained with large-scale data: DiPIR leverages pre-trained diffusion models that have a deep understanding of the world and physical phenomena through large-scale data training. Although diffusion models themselves may lack in lighting details, combined with physical rendering, they can provide valuable guidance signals to help optimize the lighting and tone mapping of the scene.
3. Personalized Adjustment
Lightweight model personalization: DiPIR provides a lightweight personalization method by making small adjustments to the pre-trained diffusion model to make it more adaptable to specific scenes and inserted objects. This personalization process enhances the model's performance in specific tasks and helps achieve more realistic object insertion results.
4. Differential Rendering and Differentiable Optimization
End-to-end differentiable rendering: DiPIR's rendering process is fully differentiable, which means that the lighting and tone mapping parameters can be optimized through back-propagation. This design allows the entire virtual object insertion process to be optimized end-to-end, thereby improving the quality of the final result.
5.Support multiple scenarios and multiple applications
Applicable to various scenes: DiPIR can be applied in various scenes, including scenes with different lighting conditions such as indoor, outdoor, day, night, etc. Whether it is the delicate lighting of indoor scenes or the high dynamic range lighting of outdoor scenes, DiPIR can effectively handle it.
Wide range of application scenarios: This method is not only suitable for virtual object insertion, but can also be used in synthetic data generation, virtual production, augmented reality and other fields, and has broad application prospects.
6. Material and tone mapping optimization
Automatically optimize material properties: In addition to lighting and tone mapping, DiPIR can also automatically adjust the material properties of virtual objects, such as metallicity, roughness, etc., to further enhance the integration of objects with the scene.Tone Mapping Matching: DiPIR can automatically adjust the tone mapping parameters of the scene to ensure that the tone of inserted objects matches the background scene, further improving realism.
Technical Methods of DiPIR
Technical Methods of DiPIR
Virtual scene construction
3D scene modeling: DiPIR creates a virtual 3D scene based on the input image, including virtual objects and proxy geometry in the scene (such as the ground plane, etc.), which is used to capture lighting effects such as shadows and reflections. The user can manually specify the position of the object, or the object position can be automatically determined by detecting the ground in the scene or using depth data.
Starting from an input image, a virtual 3D scene containing virtual objects and proxy planes is first established.
This virtual scene is designed to simulate the lighting, shadows and reflections in a real scene.
Physical Rendering
Use a physically based renderer to simulate the interaction between ambient lighting and inserted virtual objects, and how this affects the background scene (such as shadows).
The purpose of this step is to generate a physically realistic rendering effect so that virtual objects can be realistically integrated into the image.
Foreground rendering: Use a physically based path tracing algorithm to render virtual objects and generate a foreground image that is consistent with the scene lighting. This includes processing the interaction between lighting and object materials, such as reflection, refraction, etc.
Shadow ratio calculation: DiPIR calculates the shadows cast by virtual objects in the scene, and calculates the ratio of the light intensity in the shadow area by comparing the scene brightness before and after the object is inserted. This ratio is used to adjust the shadow effect of the background image to make it consistent with the insertion effect of the virtual object.
Diffusion model boot
Personalized diffusion model: The rendered image is passed to a personalized diffusion model. This diffusion model is responsible for further optimizing the image so that the virtual objects blend more naturally with the background scene. The pre-trained diffusion model is personalized to make it more suitable for specific input scenes. The powerful image generation capabilities of the diffusion model are used to guide the optimization of lighting and tone mapping parameters.
During this process, the diffusion model uses the gradient of the adapted Score Distillation
formula to feedback optimization information to help adjust the ambient lighting map and tone mapping curve.
Fractional Distillation Loss (SDS): DiPIR introduces a diffusion model-based fractional distillation loss called LDS (LoRA Distillation Sampling), which provides a feedback signal through a scene-dependent personalized diffusion model to optimize the realism of virtual object insertion. This loss function guides the optimization in the rendering process by calculating the difference between the personalized model output and the non-personalized model output.
Lighting and tone mapping optimizations
Spherical Gaussian lighting model: Scene lighting is represented by multiple spherical Gaussian (SG) functions, which are optimized to simulate the ambient lighting in the scene. The direction and intensity of the lighting are represented and calculated through this set of optimized parameters, ensuring that virtual objects can match the lighting conditions of the scene.
Dual ambient light map initialization: In the early stages of optimization, DiPIR handles the lighting consistency issue by initializing two separate ambient light maps (one for foreground objects and one for cast shadows). During training, these two maps are gradually merged into a unified ambient light map, resulting in higher lighting accuracy.
Regularization for ambient lighting fusion: By using a regularization term, DiPIR ensures the brightness and hue consistency of lighting while suppressing unnecessary ambient lighting to produce sharper shadows and more realistic lighting effects.
Differentiable Tone Mapping Curves: To match the tone mapping of the input image (usually determined by the camera sensor), DiPIR uses optimizable tone mapping curves to adjust inserted virtual objects and the shadows they cast. These curves are optimized to ensure that the color and brightness of virtual objects are consistent with the background scene.
During the entire iterative optimization process, by adjusting the ambient lighting and tone mapping curves, the lighting and tone parameters that can perfectly blend with the background scene are finally restored.
These parameters ensure that virtual objects appear as realistic as possible in pictures or videos.
Dynamic scene processing
DiPIR can also handle the insertion of virtual objects in dynamic scenes. For example, background images can be animated or the position of virtual objects can be moved to create dynamic scene effects.
Multi-view extension
The method also supports inserting virtual objects into scenes shot from different perspectives, ensuring that the objects maintain consistent lighting and blending effects under all perspectives.
Experimental Results of DiPIR
DiPIR was experimented on multiple datasets and demonstrated its superior performance in virtual object insertion tasks. The following are the main results of the experiment:
1. User Research Results
Waymo Dataset: DiPIR conducted a user study on the Waymo dataset, which contains 48 scenes covering different lighting conditions (daytime, cloudy, dusk, and night). In a comparative experiment, users were asked to choose the more realistic image between the images generated by DiPIR and other baseline methods.
Results: In all lighting conditions, images generated by DiPIR are more frequently selected by users as more realistic, especially in daytime and nighttime scenes. In the comprehensive evaluation of all scenes, DiPIR has a selection rate of over 50%, outperforming all compared baseline methods.
2. Quantitative evaluation
PolyHaven dataset: DiPIR also outperforms other baseline methods on the PolyHaven dataset, which includes 11 high dynamic range (HDR) environment maps and manually placed virtual objects, and is designed to evaluate the realism of virtual object insertion.
Metrics: The performance of each method was evaluated using quantitative metrics such as RMSE (root mean square error), SSIM (structural similarity), LPIPS (perceptual similarity), and si-RMSE (normalized root mean square error). DiPIR performed well in all these metrics, especially in SSIM and LPIPS, showing better image quality and consistency.
3. Comparison of Baseline Methods
Comparison Methods: DiPIR is compared with a variety of baseline methods, including traditional illumination estimation methods (such as Hold-Geoffroy et al. method) and generative model-based illumination estimation methods (such as StyleLight and DiffusionLight).
Results Analysis: DiPIR significantly outperforms the baseline methods, especially in complex lighting conditions (such as dusk and night scenes). StyleLight performs poorly in outdoor scenes due to inter-domain differences, while DiffusionLight, although performing well in high-frequency details, cannot predict high-intensity lighting well in daytime scenes.
4. Ablation Experiment
Ablation Analysis: To verify the contribution of each component to the performance of DiPIR, ablation experiments were performed. The study showed that removing any of the key components (such as personalization, ambient illumination fusion, and tone mapping optimization) would lead to performance degradation. In particular, when the original SDS loss was used instead of the improved LDS loss, the training process was unstable and the results were of poor quality.
5. Application display
Material and tone mapping optimization: DiPIR can not only insert virtual objects, but also optimize other properties in the scene, such as the material and local lighting of virtual objects. Experiments show that by optimizing material properties, DiPIR can make the inserted objects more visually consistent with the lighting conditions of the scene, and can adjust the tone mapping so that the color and brightness of the object are more closely matched to the scene.
Some Cases of DiPIR
DiPIR: Tone-mapping Optimization
DiPIR: Material Optimization, base color of car body
