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In this technical report, we target generating anthropomorphized personas forLLM-based characters in an online manner, including visual appearance,personality and tones, with only text descriptions. To achieve this, we firstleverage the in-context learning capability of LLMs for personality generationby carefully designing a set of system prompts. We then propose two novelconcepts: the mixture of voices (MoV) and the mixture of diffusers (MoD) fordiverse voice and appearance generation. For MoV, we utilize the text-to-speech(TTS) algorithms with a variety of pre-defined tones and select the mostmatching one based on the user-provided text description automatically. ForMoD, we combine the recent popular text-to-image generation techniques andtalking head algorithms to streamline the process of generating talkingobjects. We termed the whole framework as ChatAnything. With it, users could beable to animate anything with any personas that are anthropomorphic using justa few text inputs. However, we have observed that the anthropomorphic objectsproduced by current generative models are often undetectable by pre-trainedface landmark detectors, leading to failure of the face motion generation, evenif these faces possess human-like appearances because those images are nearlyseen during the training (e.g., OOD samples). To address this issue, weincorporate pixel-level guidance to infuse human face landmarks during theimage generation phase. To benchmark these metrics, we have built an evaluationdataset. Based on it, we verify that the detection rate of the face landmark issignificantly increased from 57.0% to 92.5% thus allowing automatic faceanimation based on generated speech content. The code and more results can befound at https://chatanything.github.io/.

ChatAnything: Facetime Chat with LLM-Enhanced Personas