  We present EchoScene, an interactive and controllable generative model that
generates 3D indoor scenes on scene graphs. EchoScene leverages a dual-branch
diffusion model that dynamically adapts to scene graphs. Existing methods
struggle to handle scene graphs due to varying numbers of nodes, multiple edge
combinations, and manipulator-induced node-edge operations. EchoScene overcomes
this by associating each node with a denoising process and enables
collaborative information exchange, enhancing controllable and consistent
generation aware of global constraints. This is achieved through an information
echo scheme in both shape and layout branches. At every denoising step, all
processes share their denoising data with an information exchange unit that
combines these updates using graph convolution. The scheme ensures that the
denoising processes are influenced by a holistic understanding of the scene
graph, facilitating the generation of globally coherent scenes. The resulting
scenes can be manipulated during inference by editing the input scene graph and
sampling the noise in the diffusion model. Extensive experiments validate our
approach, which maintains scene controllability and surpasses previous methods
in generation fidelity. Moreover, the generated scenes are of high quality and
thus directly compatible with off-the-shelf texture generation. Code and
trained models are open-sourced.
