TY - GEN
T1 - DreamAnywhere
T2 - 2026 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2026
AU - Dominici, Edoardo A.
AU - Hladky, Jozef
AU - Verhoeven, Floor
AU - Radl, Lukas
AU - Deixelberger, Thomas
AU - Ainetter, Stefan
AU - Drescher, Philipp
AU - Hauswiesner, Stefan
AU - Coomans, Arno
AU - Nazzaro, Giacomo
AU - Vardis, Konstantinos
AU - Steinberger, Markus
N1 - Publisher Copyright:
© 2026 IEEE.
PY - 2026/5/5
Y1 - 2026/5/5
N2 - Recent advances in text-to-3D scene generation have demonstrated significant potential to transform content creation across multiple industries. Although the research community has made impressive progress in addressing the challenges of this complex task, existing methods often generate environments that are only front-facing, lack visual fidelity, exhibit limited scene understanding, and are typically finetuned for either indoor or outdoor settings. In this work, we address these issues and propose DreamAnywhere , a modular system for the fast generation and prototyping of 3D scenes. Our system synthesizes a 360° panoramic image from text, decomposes it into background and objects, constructs a complete 3D representation through hybrid inpainting, and lifts object masks to detailed 3D objects that are placed in the virtual environment. DreamAnywhere supports immersive navigation and intuitive object-level editing, making it ideal for scene exploration, visual mock-ups, and rapid prototyping - all with minimal manual modeling. These features make our system particularly suitable for low-budget movie production, enabling quick iteration on scene layout and visual tone without the overhead of traditional 3D workflows. Our modular pipeline is highly customizable as it allows components to be replaced independently. Compared to current state-of-the-art text- and image-based 3D scene generation approaches, DreamAnywhere shows significant improvements in coherence in novel view synthesis and achieves competitive image quality, demonstrating its effectiveness across diverse and challenging scenarios. A comprehensive user study demonstrates a clear preference for our method over existing approaches, validating both its technical robustness and practical usefulness.
AB - Recent advances in text-to-3D scene generation have demonstrated significant potential to transform content creation across multiple industries. Although the research community has made impressive progress in addressing the challenges of this complex task, existing methods often generate environments that are only front-facing, lack visual fidelity, exhibit limited scene understanding, and are typically finetuned for either indoor or outdoor settings. In this work, we address these issues and propose DreamAnywhere , a modular system for the fast generation and prototyping of 3D scenes. Our system synthesizes a 360° panoramic image from text, decomposes it into background and objects, constructs a complete 3D representation through hybrid inpainting, and lifts object masks to detailed 3D objects that are placed in the virtual environment. DreamAnywhere supports immersive navigation and intuitive object-level editing, making it ideal for scene exploration, visual mock-ups, and rapid prototyping - all with minimal manual modeling. These features make our system particularly suitable for low-budget movie production, enabling quick iteration on scene layout and visual tone without the overhead of traditional 3D workflows. Our modular pipeline is highly customizable as it allows components to be replaced independently. Compared to current state-of-the-art text- and image-based 3D scene generation approaches, DreamAnywhere shows significant improvements in coherence in novel view synthesis and achieves competitive image quality, demonstrating its effectiveness across diverse and challenging scenarios. A comprehensive user study demonstrates a clear preference for our method over existing approaches, validating both its technical robustness and practical usefulness.
KW - diffusion models
KW - object-centric
KW - reconstruction
KW - scene generation
KW - score distillation
UR - https://www.scopus.com/pages/publications/105041249121
U2 - 10.1109/WACV61042.2026.00009
DO - 10.1109/WACV61042.2026.00009
M3 - Conference paper
AN - SCOPUS:105041249121
T3 - Proceedings - 2026 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2026
SP - 1
EP - 11
BT - Proceedings - 2026 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2026
PB - IEEE
Y2 - 6 March 2026 through 10 March 2026
ER -