# Joint Audio-Visual Generation

Duo v1 is a full-duplex world model that generates the assistant's audio and video in real time while continuously taking in the user's audiovisual input. Earlier world models largely kept intelligence outside the world, letting users steer scenes through keyboard, mouse or text prompts while nothing inside perceived the user or decided how to respond. Duo v1 cuts interaction into 17-frame chunks, about 0.71 s at 24 fps; a language intent module decides per chunk whether to speak or wait, and a causal audiovisual module generates the matching sound and picture. Of 180 checks across 30 real-time duplex cases and six binary criteria, 168 passed, including all 90 on language, speech intelligibility and visual identity, while content responsiveness reached 73.3%.

Duo v1 is a full-duplex world model that generates the assistant's audio and video in real time while continuously taking in the user's audiovisual input. Earlier world models largely kept intelligence outside the world, letting users steer scenes through keyboard, mouse or text prompts while nothing inside perceived the user or decided how to respond. Duo v1 cuts interaction into 17-frame chunks, about 0.71 s at 24 fps; a language intent module decides per chunk whether to speak or wait, and a causal audiovisual module generates the matching sound and picture. Of 180 checks across 30 real-time duplex cases and six binary criteria, 168 passed, including all 90 on language, speech intelligibility and visual identity, while content responsiveness reached 73.3%.

Observing that video and audio are perceived together yet most generative models treat them in isolation, this survey casts joint generation, cross-modal generation (one modality from the other) and joint editing as three problems defined on a single distribution over audio-visual pairs, organizes the field around one question—how the output is kept coherent across modalities in time and semantics—proposes a taxonomy along five design axes (generation strategy, audio representation, video representation, alignment enforcement, pretraining reuse), and, to the authors' knowledge, provides the first systematic taxonomy of joint audio-visual editing, mapped as nine edit categories spanning 28 edit types, together with methods, datasets and metrics for each setting and a closing list of open pr

Observing that video and audio are perceived together yet most generative models treat them in isolation, this survey casts joint generation, cross-modal generation (one modality from the other) and joint editing as three problems defined on a single distribution over audio-visual pairs, organizes the field around one question—how the output is kept coherent across modalities in time and semantics—proposes a taxonomy along five design axes (generation strategy, audio representation, video representation, alignment enforcement, pretraining reuse), and, to the authors' knowledge, provides the first systematic taxonomy of joint audio-visual editing, mapped as nine edit categories spanning 28 edit types, together with methods, datasets and metrics for each setting and a closing list of open pr