SceneFormer: Indoor Scene Generation with Transformers (2012.09793v2)

Abstract: We address the task of indoor scene generation by generating a sequence of objects, along with their locations and orientations conditioned on a room layout. Large-scale indoor scene datasets allow us to extract patterns from user-designed indoor scenes, and generate new scenes based on these patterns. Existing methods rely on the 2D or 3D appearance of these scenes in addition to object positions, and make assumptions about the possible relations between objects. In contrast, we do not use any appearance information, and implicitly learn object relations using the self-attention mechanism of transformers. We show that our model design leads to faster scene generation with similar or improved levels of realism compared to previous methods. Our method is also flexible, as it can be conditioned not only on the room layout but also on text descriptions of the room, using only the cross-attention mechanism of transformers. Our user study shows that our generated scenes are preferred to the state-of-the-art FastSynth scenes 53.9% and 56.7% of the time for bedroom and living room scenes, respectively. At the same time, we generate a scene in 1.48 seconds on average, 20% faster than FastSynth.

SceneFormer: Indoor Scene Generation with Transformers

The paper "SceneFormer: Indoor Scene Generation with Transformers" introduces an innovative approach for generating 3D indoor scenes by harnessing the capabilities of transformer models. SceneFormer positions itself distinctively in the landscape of scene generation methodologies, focusing on the autoregressive generation of object sequences conditioned on specific room layouts. This methodology leverages transformer architectures to implicitly capture the spatial relationships between objects, fundamentally differing from existing methods which often rely on annotated object relations or visual features.

Key Methodological Insights

The SceneFormer approach is centered on treating a scene as a sequence of objects, where each object is characterized by its class category, spatial location, orientation, and size. This sequence generation task benefits from the self-attention mechanisms inherent in transformers, allowing the model to contextually understand and predict these object properties in an autoregressive manner.

Key points of differentiation include:

• Implicit Object Relations: Unlike previous approaches, SceneFormer does not require manual annotation of object relationships. Instead, it learns these relationships implicitly through the transformer’s self-attention mechanism. This reduces potential biases and streamlines the data processing pipeline.
• Flexibility in Conditioning: The model is capable of generating scenes based on multiple conditional inputs. It can operate with room layouts that specify the spatial framework, or it can be directed through textual descriptions to fill a room with appropriate objects.
• Efficient Scene Synthesis: SceneFormer boasts a notable efficiency in scene generation, achieving an average scene generation time of 1.48 seconds, which is 20% faster than the contemporary FastSynth method. This performance is achieved without sacrificing the realism of the scenes, as evidenced by a user paper where scenes generated by SceneFormer were preferred over those from FastSynth 53.9% of the time for bedrooms and 56.7% for living room scenarios.

Evaluation and Implications

The effectiveness of SceneFormer is substantiated through robust comparative analyses. The paper conducts perceptual studies where generated scenes are evaluated for realism against state-of-the-art methods like DeepSynth, FastSynth, and PlanIT. SceneFormer's output was consistently preferred, highlighting its ability to generate complex, aesthetically pleasing scenes that better adhere to human perception of realistic interior design.

From a theoretical standpoint, SceneFormer contributes to the understanding of transformer applications beyond traditional language and image tasks. It showcases the adaptability of transformers in capturing the nuanced dependencies between spatial entities in 3D environments. Practically, the method is poised to impact fields such as virtual reality, real estate visualization, and interior design, where quick and realistic scene rendering is crucial.

Future Directions

The adaptability and performance of SceneFormer spotlight several avenues for future research:

• Joint Conditioning: Exploring models that can simultaneously handle multiple forms of conditioning, such as integrating both textual and spatial input, could enhance the model’s applicability.
• Integration of Visual Data: Although SceneFormer achieves its goals without visual information, incorporating 2D or 3D visual data could further enhance the realism and coherence of generated scenes.
• Application to Diverse Domains: Expanding beyond residential interior scenes to encompass other environments, such as office spaces or public areas, would test the model’s generality and robustness.

In summary, SceneFormer demonstrates a compelling use case for transformer models in 3D scene generation, offering efficient and flexible solutions for generating realistic indoor environments. This paper not only elevates the discourse on scene synthesis techniques but also provides a foundation for further exploration and innovation in the field of AI-driven visual content creation.