3D Object Manipulation in a Single Image using Generative Models (2501.12935v1)

Abstract: Object manipulation in images aims to not only edit the object's presentation but also gift objects with motion. Previous methods encountered challenges in concurrently handling static editing and dynamic generation, while also struggling to achieve fidelity in object appearance and scene lighting. In this work, we introduce \textbf{OMG3D}, a novel framework that integrates the precise geometric control with the generative power of diffusion models, thus achieving significant enhancements in visual performance. Our framework first converts 2D objects into 3D, enabling user-directed modifications and lifelike motions at the geometric level. To address texture realism, we propose CustomRefiner, a texture refinement module that pre-train a customized diffusion model, aligning the details and style of coarse renderings of 3D rough model with the original image, further refine the texture. Additionally, we introduce IllumiCombiner, a lighting processing module that estimates and corrects background lighting to match human visual perception, resulting in more realistic shadow effects. Extensive experiments demonstrate the outstanding visual performance of our approach in both static and dynamic scenarios. Remarkably, all these steps can be done using one NVIDIA 3090. Project page is at https://whalesong-zrs.github.io/OMG3D-projectpage/

• The paper presents the OMG3D framework that converts 2D images into controllable 3D objects and supports dynamic transformations through generative diffusion models.
• It introduces the CustomRefiner module, which uses a specialized diffusion model for texture refinement, ensuring high fidelity and consistent appearance across viewpoints.
• The IllumiCombiner module accurately estimates and adjusts lighting from background imagery, significantly enhancing photorealism and visual realism in generated outputs.

Overview of "3D Object Manipulation in a Single Image using Generative Models"

This paper introduces "OMG3D," a novel framework designed to facilitate 3D object manipulation within a single image through the utilization of generative models. The research targets the dual aspect of enhancing static image editing capabilities while also introducing dynamic motion to objects, achieving high fidelity in visual rendering. The framework is comprehensive, integrating precise geometrical control with the potent generative capabilities of diffusion models.

The method outlined begins with converting 2D objects into 3D forms, thereby allowing user-directed modifications that can introduce lifelike motions. To address the complexities in achieving realistic textures and lighting, the framework includes modules such as CustomRefiner and IllumiCombiner. CustomRefiner ensures that the textures of 3D models align closely with the original imagery by pre-training a customized diffusion model, focusing on matching details and styles. Meanwhile, IllumiCombiner addresses lighting concerns by estimating and adjusting background lighting to align with human visual perception, enhancing shadow quality and overall realism.

Key Contributions and Methods

• OMG3D Framework: This unified platform enables both the static and dynamic generation of objects in 3D space, facilitating transformations and animations. By bridging static transformations and enhancing temporal dynamics, OMG3D improves upon existing methodologies that struggle to maintain consistency across object appearances and motions.
• CustomRefiner Module: Designed to refine textures, this module employs a specially trained diffusion model to enhance the quality of 3D renderings. It employs techniques such as differentiable rasterization to optimize textures across various viewpoints, ensuring fidelity to the original image.
• IllumiCombiner Module: For realistic rendering of lighting effects, this module estimates and adjusts lighting from background imagery. By deriving spherical light outputs that maintain accurate color fidelity and enhance intensity as needed, IllumiCombiner significantly boosts the photorealism of generated images and videos.

Numerical and Experimental Outcomes

The paper presents extensive experimental results demonstrating OMG3D's superiority over existing methods in both static image editing and image-to-video generation tasks. The framework performs efficiently, utilizing a single NVIDIA 3090 GPU, which underscores its practicality for real-world applications.

A comparative analysis highlights OMG3D's advantages in maintaining higher-fidelity object appearances and producing convincing dynamic effects. The user evaluations indicate a preference for OMG3D outputs over baseline alternatives, suggesting that the framework's innovations in texture and lighting refinement offer tangible improvements in visual results.

Theoretical and Practical Implications

From a theoretical perspective, this work advances the discourse on integrating 2D and 3D image processing techniques, opening pathways for more nuanced and adaptable generative models. The successful implementation of modules like CustomRefiner and IllumiCombiner demonstrates the potential for nuanced control over visual elements such as texture and light, which are often marginalized in image manipulation studies.

Practically, OMG3D's approach could significantly impact areas where image manipulation is crucial, such as augmented reality, virtual reality, film, and design industries. By improving ease of use and output quality, this framework could democratize access to advanced image editing capabilities traditionally reserved for expert users or high-budget projects.

Future Directions

The research suggests several avenues for further exploration and enhancement. Future work could focus on extending OMG3D's capabilities to handle more complex scenes and multiple interacting objects, potentially enhancing the framework's applicability to environments with dynamic interactions. Additionally, refining the integration of advanced lighting models could push the boundaries of what is achievable in rendering photorealistic scenes.

In summary, "3D Object Manipulation in a Single Image using Generative Models" represents a significant step forward in object manipulation techniques, providing deeper insights into the merger of geometric precision and generative prowess. The contributions made by this framework set new standards for fidelity and realism in visual rendering, paving the way for future innovations in the field of AI-assisted image processing.