Diffusion TV: Experiencing Diffusion Models through Tangible, Embodied Interaction
On the map: Diffusion models → Diffusion and flow policies for robotics
Abstract (arXiv)
Diffusion TV is an interactive AI art installation that offers a tangible and embodied experience of diffusion models through a modified CRT TV. By physically manipulating the TV's antenna, audiences control the clarity of AI-generated images and sounds, metaphorically enacting the denoising process that underlies diffusion-based generation. Using the tuning knob, participants switch between three channels featuring AI-generated animals from the Past (extinct species), Present (endangered species), and Future (speculative creatures), situating the interaction within a temporal and ecological narrative. Through continuous audiovisual feedback and physical interaction, Diffusion TV foregrounds the generative process over final outputs, allowing audiences to explore intermediate states as experiential material. Rather than providing explicit technical explanation, the work presents an alternative, embodied mode of explainable AI that invites exploratory engagement with and reflection on generative technologies.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 2 of its 16 references that are in the map. First posted on arXiv 4 Sep 2026.
Builds on
2 of 16 references.
References · 2
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
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Close in wording, no citation either way
- 2026Unboxing Diffusion Models for the Arts: Interactive Model Bending and Practice-Based Explainability
- 2024Interactive Visual Learning for Stable Diffusion
- 2023State of the Art on Diffusion Models for Visual Computing
- 2023Imitating Human Behaviour with Diffusion Models
- 2025Diffusion Explorer: Interactive Exploration of Diffusion Models
- 2024Video Diffusion Models: A Survey
- 2025X-Diffusion: Training Diffusion Policies on Cross-Embodiment Human Demonstrations
- 2026Real-Time AttentionBender: Granular Interactive Network Bending of Video Diffusion Transformers